A patient’s search for answers reveals an uncomfortable truth about complex systems: when information is fragmented, people can follow the process and still get stuck. From transplant care to medical billing, are we fixing problems—or simply getting better at cleaning up the mess?
“Whatever the organ, most patients don’t know what to
ask,” said Dr.
Allyson Hart, a researcher at Hennepin Healthcare Research Institute,
in Associated Press
It is a simple observation with a much bigger implication.
What happens when people must navigate a complicated system
without understanding how it works, which options are available, or what is
standing in their way?
Sometimes, the problem is not a lack of effort.
Sometimes, it is not a lack of expertise.
And sometimes, it is not even a lack of technology.
Sometimes, people simply cannot see the information they
need to make their next move.
That is the question raised by the experience of Katherine
Screws, a Phoenix woman whose three-year wait for an intestine transplant
illustrates the human consequences of navigating a complicated healthcare
system.
Her story also raises a question for physician-owned
practices: How much administrative frustration could be avoided if the right
information became visible before the next step went wrong?
Because healthcare has an impressive collection of software,
portals, databases, dashboards, and artificial intelligence.
Yet somehow, people still end up asking the digital
equivalent of, “Has anyone seen the paperwork?”
Apparently, the paperwork has achieved interoperability with
everyone except the person who needs it.
A six-month expectation became a three-year wait
In May 2022, one month after marrying her husband, Stephen
Gerrish, Katherine Screws was involved in a car crash that destroyed her
small intestine.
She could no longer absorb nutrition normally. She depended
on overnight intravenous nutrition, often felt too sick to get out of bed, and
could not return to her engineering career or pursue her plans to start a
family.
She had been told that a transplant might be possible within
approximately six months.
Instead, she waited three years.
During that time, Screws faced more than a medical
challenge. She was trying to understand a system whose options, processes, and
decisions were not always clear to her.
The turning point came when she connected with Lisa
Stocks, a nurse at California's organ procurement organization, OneLegacy.
Stocks helped Screws understand that transplant centers can
differ in their practices and in which donor organs they are willing and able
to accept. Screws also learned about publicly available information that could
help her compare transplant centers.
She explored the Scientific Registry of Transplant
Recipients database and registered at MedStar Georgetown University Hospital
in Washington, D.C., in addition to her original center, UCLA Health.
In June 2025, just weeks after registering at Georgetown,
she received her intestine transplant.
Dr. Cal Matsumoto, a transplant surgeon at MedStar
Georgetown University Hospital, and his team performed the procedure. The donor
intestine was longer than usual, but it fit successfully.
The story also features Dr. Syed Ali Husain of NYU
Langone Health, who researches differences in transplant practices, and Dr.
Allyson Hart, who helped develop patient-friendly tools for comparing
transplant centers.
UCLA Health declined to comment on Screws's individual care,
citing patient privacy rules, but said it follows federal transplant guidelines
and continually strives to improve its processes.
Screws was careful to explain that her frustration was not
simply directed at her original physicians. She was troubled by the lack of
clear, centralized information about what to expect and what might be holding
up progress.
Her experience does not prove that every transplant delay is
avoidable or that one hospital's decisions are inherently better than
another's. Transplantation involves difficult clinical judgments, complex
logistics, and time-sensitive matching.
But it does demonstrate the importance of understanding how
a system works and knowing what questions to ask.
The most important question may not be, “Why is this
taking so long?” It may be, “What is actually holding this up—and what can I do
about it?”
That question belongs in many more places across healthcare.
The contrarian argument: Maybe healthcare doesn't need
another dashboard
When something goes wrong, the instinct is often to add
something.
Another application. Another workflow. Another reporting
tool. Another automated notification. Another dashboard showing that the other
dashboard has a problem.
Before long, the organization has a dashboard for the
dashboard.
But what if the underlying problem is not always the absence
of a tool?
What if the information already exists somewhere—but is
incomplete, disconnected, outdated, difficult to interpret, or unavailable to
the person who needs it?
Consider how a medical practice operates.
The physician understands the patient's clinical
circumstances. The front office manages registration. The authorization team
works with payer requirements. The scheduler coordinates appointments. The
medical biller needs the documentation and information required to submit an
accurate claim.
Each person may be doing their job correctly.
Yet the overall process can still fail if important
information does not reach the next person at the right time.
A patient's coverage may have changed. A referral may be
missing. An authorization may still be pending. A documentation requirement may
not have been identified until after the encounter.
The practice discovers the problem later, when correcting it
is more expensive and disruptive.
This is the distinction between optimizing a task and
improving the system that connects the tasks.
A faster process can still be a bad process. An automated
error is still an error. And an AI-generated answer cannot make missing
information magically appear.
Technology can improve healthcare enormously. But adding
technology without addressing the quality and continuity of information may
simply help an organization process its existing problems more efficiently.
That is progress of a sort.
It is just not necessarily the progress we need most.
What Katherine's experience can teach us—and what it
cannot
An intestine transplant and a rejected medical claim are not
equivalent.
The medical stakes, ethical considerations, clinical
decisions, and consequences are fundamentally different. Katherine's story
should not be used to suggest that a billing platform could have shortened her
transplant wait, nor should it be interpreted as an accusation of negligence
against the professionals involved.
The connection is more specific.
People need understandable, timely information to
navigate complex systems and make informed decisions.
For a transplant patient, that may mean understanding how
centers differ, which options are available, and what questions to discuss with
a transplant team.
For a physician-owned practice, it may mean knowing whether
a referral is complete, whether authorization is confirmed, whether coverage
information is current, and whether documentation supports the service being
billed.
Different situations. Different stakes. A shared challenge:
information must be available and understandable before it can guide action.
In both settings, the existence of a process does not
guarantee that the people navigating it can see how it works.
That is why information transparency matters.
The hidden cost of discovering problems too late
Imagine a physician evaluating a patient and determining
that a procedure is medically appropriate.
The appointment is scheduled. The clinical note is written.
The patient expects the next step to happen.
But several administrative questions remain:
- Is the
insurance information current?
- Does
the patient's plan cover the service under the applicable conditions?
- Is
prior authorization required?
- Is a
referral necessary?
- Does
the documentation contain the information required to support the request?
- Has
the authorization status reached the scheduling team?
- Will
the eventual claim accurately reflect the service provided and documented?
These questions may involve different staff members,
systems, and payer requirements.
If one requirement is missed, the problem might not become
visible until the procedure is scheduled, the service is performed, or the
claim is submitted.
Then the practice must investigate.
Someone retrieves the documentation. Someone contacts the
payer. Someone follows up on the authorization. Someone corrects the claim.
Someone explains the delay to the patient. Someone tries to determine why the
process failed.
And the physician may be asked to clarify a note weeks after
the encounter.
The organization has now paid for the work twice: once to
perform the original task and again to repair the process.
This is where administrative friction becomes a financial
and operational problem.
A denied claim is not just a line in a report. It can
represent staff time, delayed reimbursement, additional patient communication,
and work that could have been directed elsewhere.
The critical question is not merely, “How do we fix this
claim?”
It is:
“What happened before the claim that made this problem
possible?”
That is the upstream question.
The prior authorization problem is not just paperwork
The American Medical Association's prior authorization
research has documented substantial administrative burdens for physicians and
patients.
Its 2025 physician survey, reported in 2026, found that:
- 95% of
surveyed physicians said prior authorization delays access to necessary
care.
- 92%
said it negatively affects clinical outcomes.
- 79%
said patients abandon treatment because of authorization challenges.
- 94%
said prior authorization contributes to physician burnout.
- Physicians
and staff reported spending an average of 13 hours per week dealing with
prior authorization.
These are survey findings, not proof that every delay causes
harm or that one technology solution can eliminate the burden. But they
illustrate how much effort the process can consume.
For a small practice, 13 hours a week is substantial.
That time has to come from somewhere: patient communication,
referral coordination, follow-up, documentation, or other work that also
matters.
And the irony is hard to miss. Healthcare continues to
invest in digital transformation while physicians and staff spend hours
navigating administrative requirements across fragmented systems.
We have digitized many processes.
We have not necessarily connected them.
Three lessons from the people in Katherine's story
1. Katherine Screws: People cannot act on options they
cannot see
Screws's experience shows why accessible information
matters.
For practices, the equivalent question is whether each
authorized team member can see the information needed to move work forward.
Can the scheduler see that authorization is pending?
Can the physician understand which documentation requirement
remains unresolved?
Can the biller distinguish a genuine coding question from an
eligibility issue?
Can the practice owner identify recurring problems without
manually reconstructing events from multiple systems?
The answer is not to give everyone unrestricted access to
everything. Privacy, security, and role-based access are essential.
The objective is to make relevant information available to
the right person at the right time.
2. Lisa Stocks: Experience is valuable—but it should not
live only in someone's memory
Stocks helped Screws understand the transplant system and
explore additional options.
Medical practices also depend on people with experience. A
skilled biller may know that a particular payer repeatedly requests certain
documentation. A referral coordinator may understand which records a specialist
requires. A front-office employee may recognize a recurring eligibility
problem.
That knowledge is valuable.
But if it exists only in someone's memory, a personal
spreadsheet, or an old email thread, the organization cannot reliably apply it
across future encounters.
The solution is not to replace experienced employees.
It is to preserve useful knowledge, make recurring
requirements visible, and reduce dependence on memory alone.
Even an outstanding employee cannot simultaneously be the
practice's database, notification system, and institutional memory.
3. Dr. Syed Ali Husain, Dr. Allyson Hart, and Dr. Cal
Matsumoto: Context changes how information should be interpreted
The transplant reporting describes differences among centers
and the clinical judgment involved in evaluating donor organs.
That reinforces a broader principle: A decision cannot be
understood simply by looking at its outcome without considering the
circumstances behind it.
The same is true of medical billing.
A denial does not automatically mean a coder made a mistake.
A missing authorization does not necessarily mean a scheduler was careless. A
documentation gap does not automatically mean a physician failed to provide
appropriate care.
The cause could involve changing payer requirements,
incomplete registration, a communication breakdown, a documentation issue, or a
process that failed to make an important requirement visible.
Counting failures tells us what happened. Understanding
their causes tells us what to change.
That is the difference between reporting a problem and
learning from it.
The myth: More AI automatically means less administrative
work
AI can help identify patterns, summarize information, flag
potential inconsistencies, and support routine tasks.
But AI is not a substitute for good data, appropriate
clinical judgment, or a well-designed process.
Imagine an automated system submitting an authorization
request with incomplete information. It can submit the request quickly, but the
payer may still return it for missing details.
Imagine an AI tool generating a polished clinical note that
omits a fact necessary to explain the service. The language may sound excellent
while the underlying problem remains.
Imagine a denial-management platform that helps staff
categorize rejected claims. That may be valuable, but categorizing the same
recurring problem does not necessarily prevent it from happening again.
None of these tools is inherently bad. Each may address part
of the problem.
The mistake is assuming that automating one step
automatically improves the entire process.
In a September 30, 2026, joint statement, six major
physician organizations—including the American Medical Association, the
American College of Physicians, and the American College of Surgeons—warned
against treating AI as a replacement for physician expertise. They emphasized
that clinical context, professional judgment, patient safety, and
accountability must guide the development and use of AI.
The lesson for healthcare administration is equally
important: Technology should support better decisions, not simply accelerate
activity.
Before adopting an AI tool, ask:
- Does
it help identify the underlying issue?
- Does
it use reliable, relevant information?
- Does
it explain what remains unresolved?
- Does
it help the appropriate person take the next step?
- Can
the practice audit its outputs and correct mistakes?
- Does
it preserve human oversight where judgment is required?
If the tool only makes the existing queue move faster, it
may still be useful. But it is solving a different problem from preventing
avoidable work.
What upstream billing intelligence looks like in practice
Upstream billing intelligence starts with the idea that many
preventable billing problems have contributing conditions that exist before a
claim is submitted.
Those conditions can arise during registration, eligibility
verification, scheduling, referral management, authorization, documentation,
coding, or charge capture.
A practical framework is:
Capture → Structure → Preserve → Propagate → Act → Learn
Capture: Collect relevant information when it becomes
available.
Structure: Organize it so people and systems can
interpret it consistently.
Preserve: Retain the context behind important
decisions, requirements, and exceptions.
Propagate: Make the information available to
authorized people and downstream systems that need it.
Act: Flag unresolved requirements and help the
responsible person determine the next appropriate step.
Learn: Analyze recurring patterns and improve the
process based on evidence.
For example, if a procedure requires authorization, the
practice should be able to distinguish an authorization that is requested from
one that is approved or confirmed as unnecessary.
If documentation appears to be missing a required element,
the system should flag the issue for appropriate review rather than silently
assuming the record is complete.
If a payer repeatedly requests the same information, the
practice should investigate whether a template, training, handoff, or process
needs improvement.
This does not mean every denial can be prevented. Some
result from payer decisions, clinical disagreements, changing rules, or
circumstances outside the practice's control.
It means the practice should identify and address the
preventable conditions it can influence.
The best time to discover a missing requirement is
generally before additional work depends on it.
Seven metrics that reveal whether the process is
improving
A practice does not need 47 dashboard widgets to start
making progress. It needs a few consistent measures that lead to useful
decisions.
- First-pass
claim acceptance: How often do claims pass initial submission checks
without preventable errors? Distinguish clearinghouse acceptance from
payer adjudication.
- Denial
rate by root cause: Separate authorization, eligibility, coding,
documentation, and other relevant causes rather than relying only on the
overall rate.
- Rework
hours: Measure staff time spent correcting preventable errors,
retrieving information, and resubmitting claims.
- Authorization
lead time: Track the time required to move from a complete request to
a documented decision, separating payer delays from internal delays.
- Missing-information
rate: Measure how often work reaches a critical step without the
information required to proceed.
- Repeat-error
rate: Determine how frequently the same underlying problem recurs
after it has been addressed.
- Patient-impact
indicators: Where appropriate, monitor avoidable rescheduling, care
delays, unexpected financial communications, and related outcomes.
Establish a baseline, define each measure consistently, and
review the results regularly.
The goal is not to make the dashboard prettier.
It is to answer a more important question:
Are we preventing more problems—or merely getting better
at cleaning them up?
A four-week experiment for a small medical practice
Improving a process does not necessarily require purchasing
a new platform.
Start with one recurring problem.
Week 1: Find the pattern.
Review a sample of recent denials, authorization delays, or
claim corrections. Identify where the issue first appeared.
Week 2: Map the information.
Determine where the required information originates, who
verifies it, where it is stored, who needs it next, and how the current process
fails.
Week 3: Test one change.
Try a targeted checklist, a standardized field, a clearer
handoff, or an appropriate alert. Avoid adding steps that do not address a
defined problem.
Week 4: Measure the result.
Compare the results with the baseline. Review error
frequency, rework hours, delays, and staff feedback. Check for unintended
consequences.
If the change helps, refine it. If it does not, investigate
why before automating it.
This is not the most glamorous AI demonstration. Nobody is
going to make a viral video about a missing authorization field that was
completed correctly the first time.
But that is precisely the point.
A problem prevented is often less visible than a problem
repaired.
It is also frequently less expensive to deal with.
Where OnnX 2.0 fits
This is the problem OnnX 2.0 is being developed to address.
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.
Built by physicians and medical billers since 1995, the OnnX
2.0 concept focuses on the information and decisions that influence billing
before a claim becomes a problem.
Clinical context, patient information, coverage,
authorization requirements, referrals, documentation, and operational decisions
can become fragmented across people and systems.
When the relevant information does not travel with the work,
downstream teams must reconstruct it.
OnnX 2.0 is being developed around a different approach:
make important context more structured, visible, and actionable so
physician-owned practices can recognize information gaps earlier and
investigate risks before they become avoidable rework.
The objective is not to promise zero denials or claim that
every billing problem can be eliminated. It is to focus on upstream causes that
a practice may be able to identify and address.
That means asking different questions.
Instead of only asking how to process a denial faster, ask
what created the conditions for it.
Instead of only asking how to chase missing information, ask
why it was missing when it was needed.
Instead of buying another dashboard, ask which decision the
dashboard should help someone make.
The aim is to move from reactive billing toward more
predictable, informed revenue-cycle operations.
That is a development vision, not a claim that every
capability has already been validated in practice. The value must ultimately be
demonstrated through real workflows, measurable results, and appropriate
safeguards.
But the principle is worth pursuing: Better information
upstream can create better opportunities to prevent problems downstream.
The future of healthcare may depend on what we stop
having to fix
Katherine Screws's experience reminds us that navigating a
complicated system can be difficult even when the people inside it are working
hard.
Her story does not prove that every delay is avoidable. It
does show why accessible information, clear explanations, and an understanding
of available options matter.
For physician-owned practices, the parallel is not the
severity of the outcome. It is the importance of visibility and informed
action.
A patient should not have to guess what question to ask
next.
A physician should not repeatedly reconstruct the context
behind a clinical decision.
A medical biller should not have to become a detective
because essential information is scattered across disconnected systems.
And a practice owner should not have to wait until the end
of the month to discover a recurring problem that could have been visible much
earlier.
Healthcare does not need technology for technology's sake.
It needs systems that help people do the right work with the
right information at the right time.
We have spent years teaching technology to process more
work.
Perhaps the next breakthrough will come from designing
systems that recognize which problems should never have reached the next step
in the first place.
And if that sounds less exciting than another AI
announcement, consider this:
Sometimes, the best result is a claim that never needed
correcting, an authorization that did not stall, or a patient who did not have
to make five phone calls to discover what happens next.
No dramatic alert. No heroic spreadsheet. No billing
department operating like a crime-scene investigation unit.
Just a process that works better.
That may not be flashy. But for physicians, staff, and
patients, it could be far more valuable.
Frequently asked questions
What is upstream billing intelligence?
It is an approach to identifying and addressing information
gaps, requirements, and operational risks before they contribute to claim
errors, denials, or rework.
Why do billing problems begin before a claim is
submitted?
Claims depend on information collected across registration,
eligibility, referrals, authorization, documentation, coding, and charge
capture. Errors or omissions at earlier stages can affect the claim later.
Can AI prevent medical billing denials?
AI may help identify patterns, missing information, and
potential inconsistencies. It cannot guarantee payment or eliminate every
denial. Results depend on data quality, payer rules, workflow integration, and
appropriate human oversight.
What should a small practice do first?
Choose one recurring problem, identify where it originates,
map the information and handoffs involved, test one targeted improvement, and
measure whether the change reduces errors or rework.
Is OnnX 2.0 a traditional medical billing service?
OnnX 2.0 is being developed as an upstream intelligence
platform focused on helping physician-owned practices improve the information
and decisions that affect billing. Its intended positioning emphasizes
prevention and intelligence rather than outsourced billing services.
Join the conversation
Katherine Screws's story raises a question that reaches far
beyond transplant medicine:
Are we building smarter healthcare systems—or simply
getting better at managing the problems our systems keep creating?
Physicians, practice owners, administrators, and medical
billers: Where does the most avoidable rework begin in your
experience—registration, eligibility, authorization, documentation, coding, or
the handoffs between them?
Share your perspective in the comments. If this challenges
how we think about healthcare administration, share or repost it so others can
join the discussion.
About the author
Daniel Cham, MD is a physician, entrepreneur, and
healthcare technology founder developing OnnX 2.0, an upstream intelligence
platform for healthcare built by physicians and medical billers since 1995. His
work focuses on improving the quality and continuity of information across
clinical and administrative processes so physician-owned practices can reduce
preventable billing problems and focus more attention on patient care.
Disclaimer: This article is for educational and
informational purposes only and does not provide medical, legal, coding,
billing, or reimbursement advice. Katherine Screws's experience is discussed
based on public reporting and should not be interpreted as an allegation of
wrongdoing by any individual or organization. The comparison with medical
billing concerns information and coordination, not equivalence between clinical
outcomes and administrative delays.
References
- Associated
Press, October 20, 2026: Arizona
woman's three-year wait for an intestine shows the US organ transplant
system's complexity.
- Associated
Press, October 20, 2026: How the
US organ transplant system works and what to ask to improve your chances.
- American
Medical Association: Survey:
Prior authorization reform pledge falls short for physicians.
- American
Medical Association, September 30, 2026: Statement
from leading physician organizations on the role of augmented intelligence
in healthcare.
- Agency
for Healthcare Research and Quality: Care
Coordination.
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