The best workflow is the one you never needed. Through Katherine Elkins’s journey from nurse to patient and back, we confront healthcare’s overlooked challenge: preventing unnecessary work before it reaches the people who can least afford it.
“I can ensure they have dignity, and that their death is
a phenomenal, beautiful thing.” — Courtney,
a hospice nurse practitioner, interviewed by Madeleine Aggeler for The
Guardian.
The Nurse Who Became the Patient
Imagine spending your career helping people navigate some of
the most vulnerable moments of their lives—then suddenly finding yourself on
the other side of the hospital bed.
That is what happened to Katherine Elkins, a nurse at Touro
in New Orleans.
In 2023, Elkins was riding her bicycle with friends to
attend Jazz Fest when a car struck her at an intersection. Her next clear
memory was waking up at University Medical Center New Orleans. Her family had
been warned that she might not survive the night.
She survived. But survival was only the beginning.
The accident left her with a traumatic brain injury, brain
hemorrhaging, fractures, a collapsed lung, severe nerve damage and other
serious injuries. Her recovery required intensive rehabilitation, physical and
occupational therapy, speech therapy, additional procedures and countless
medical appointments.
Everyday tasks became difficult. Concentrating was harder.
Multitasking became a challenge. Even managing routine responsibilities could
feel overwhelming.
Then came the bills.
Elkins described how the brain injury made it difficult to
manage the administrative responsibilities that accompanied her recovery.
Medical bills arriving in the mail became a source of anxiety. Tasks that might
once have seemed ordinary could suddenly feel impossible.
Think about that for a moment.
A person who had spent years caring for patients now had to
navigate the system as a patient herself—while recovering from an injury that
made complex tasks harder to manage.
Fortunately, she was not alone.
Elkins recalled a nurse who learned that she loved Dippin’
Dots and brought them to her. Another caregiver, identified in the published
account only as Jesse, helped her get outside to experience the campus
fountain.
Small gestures. Enormous meaning.
In April 2026, three years after the accident, Elkins
returned to nursing at Touro with workplace accommodations and support from her
colleagues.
Her story is one of survival, rehabilitation, human
connection and determination.
But it also raises a question healthcare leaders should be
willing to confront:
Why can receiving care sometimes create a second job for
the person who is already struggling to recover?
The first job is getting better.
The second can involve understanding bills, organizing
documents, coordinating appointments, responding to insurance requests and
figuring out what to do when information is missing or confusing.
Nobody puts “unpaid administrative project manager” on a
patient’s discharge paperwork.
Yet patients and families can find themselves doing exactly
that.
The Contrarian Truth: More Automation Is Not Always More
Progress
Healthcare technology has a compelling promise: automate the
workflow, save time, reduce costs and improve efficiency.
There is genuine value in that ambition. Automation can
reduce repetitive work, improve consistency and free staff to focus on tasks
that require human judgment.
But the industry has a blind spot.
We are getting better at automating work without always
asking whether the work should exist in the first place.
Consider a hypothetical physician-owned practice.
A patient arrives for an appointment. Staff enter
information into the electronic health record. Another employee checks the
practice management system. Someone notices a missing field. A message is sent
to request clarification. The information is entered again.
Later, a claim is submitted.
The payer rejects it because the original information was
incomplete or inconsistent.
Now the practice has a new workflow:
- Investigate
the rejection.
- Identify
the missing information.
- Contact
the appropriate person.
- Wait
for a response.
- Correct
the record.
- Resubmit
the claim.
- Monitor
its status.
- Repeat
the process if the correction does not solve the problem.
An automation vendor may offer to accelerate several of
these steps.
That could help.
But what if the information could have been captured
correctly at the beginning?
What if the missing requirement could have been identified
before submission?
What if the handoff had been designed to prevent the error
rather than discover it weeks later?
The most valuable workflow might be the one that never
needed to happen.
Automating rework can make rework faster. It does not
necessarily make rework disappear.
A beautifully automated mistake is still a mistake. It
simply arrives with better formatting and a more impressive dashboard.
And a claim that has been submitted is not the same as a
claim that has been paid.
Activity is not completion. Processing is not resolution. A
task marked complete is not proof that the underlying problem has been solved.
Healthcare can become extraordinarily efficient at managing
the consequences of poor information while leaving the source of the problem
untouched.
That is not necessarily progress. Sometimes it is just
expensive repetition at scale.
What Katherine Elkins’s Story Actually Teaches Us
We should be careful not to turn Elkins’s experience into a
convenient technology sales pitch.
Her accident was not caused by medical billing. Her story
does not establish that billing inefficiencies caused her recovery challenges,
nor does it prove that a particular software platform could have prevented her
anxiety.
Her recovery involved medical professionals, rehabilitation
specialists, colleagues, family and community members. Their contributions
mattered in different ways.
But her experience illustrates a broader human reality:
Administrative tasks do not become less demanding simply
because someone has more important things to deal with.
They may become more difficult.
A person recovering from a brain injury may struggle with
concentration and complex tasks. Someone undergoing cancer treatment may have
limited energy. A caregiver may be balancing work, family responsibilities and
a loved one’s appointments.
Even people who are generally organized can find
administrative work overwhelming during a crisis.
The problem is not necessarily that people lack discipline.
It is that systems often assume everyone has unlimited time,
attention, organizational capacity and emotional energy.
They do not.
Neither do clinicians.
A physician who spends an evening resolving documentation
discrepancies is not spending that time with a patient. A medical assistant
repeatedly chasing missing information has less capacity for other work. A
billing specialist correcting preventable errors cannot devote the same
attention to more complex reimbursement problems.
The cost is not merely the time spent fixing something.
It is the opportunity cost of everything that could have
been accomplished instead.
A problem may begin at the point of care, become a staff
follow-up task, turn into a claim delay and eventually require attention from a
patient or family member.
The person who experiences the final inconvenience may be
far removed from the process where the problem began.
That is why healthcare leaders should ask two questions:
- How
quickly did we resolve the problem?
- Why
did the problem need to exist in the first place?
The first question measures operational response.
The second opens the door to prevention.
Healthcare Has Made Progress. So Why Is There Still So
Much Friction?
The healthcare industry is not standing still.
The 2025 CAQH Index, released in February 2026, estimated
that electronic transactions and improved data exchange helped U.S. healthcare
avoid $258 billion in administrative costs during 2024. The report also
identified an estimated $21 billion in additional annual savings potential
from fully automating remaining manual and partially manual administrative
transactions.
These are national industry estimates, not a guarantee of
savings for any individual practice.
They nevertheless highlight an important point: automation
has created substantial value, but administrative inefficiency remains.
The wrong conclusion would be that automation has failed.
The more useful question is whether the next phase of
improvement should focus only on automating more transactions—or also on
reducing the errors, omissions and handoff failures that create avoidable
transactions in the first place.
Automation is a means, not the outcome.
The desired outcome is less unnecessary work, more reliable
information, fewer avoidable delays and more time for people to do work that
genuinely requires their expertise.
A clinic should not have to celebrate how quickly it
corrected a recurring error if the same error could have been prevented.
It should investigate why the error keeps happening.
That may sound less exciting than announcing another
artificial intelligence initiative. It is also more likely to produce a useful
operational question.
The next breakthrough may not be doing the same work
faster. It may be discovering how much of that work was unnecessary.
The Hidden Cost of “Just One More Follow-Up”
Administrative problems often look small when viewed
individually.
A missing date. An incomplete field. A discrepancy between
systems. A document that was not attached. An authorization request that needs
clarification.
Each issue may take only a few minutes to address.
But those minutes rarely tell the whole story.
Someone must notice the problem, understand it, locate the
relevant information, determine who can resolve it, make contact, wait for a
response and verify that the correction worked.
The process may involve several employees and multiple
systems.
There is also the cost of interruption. A staff member who
stops one task to chase missing information must later return to the original
task and reconstruct their train of thought.
Multiply this across a busy practice, and seemingly minor
issues can fragment an entire day.
The pattern is familiar:
Incomplete information → follow-up → delay → correction →
resubmission → more follow-up.
It is tempting to treat this cycle as an unavoidable feature
of healthcare.
Some administrative complexity is unavoidable. Clinical
decisions require judgment. Payer rules differ. Unusual cases require
investigation. Not every denial is preventable.
But other work exists because information was not captured
clearly, expectations were not aligned, responsibilities were ambiguous or
systems failed to communicate.
Those are different problems. They deserve different
solutions.
A workflow automation tool may help move a task from one
person to another.
A well-designed upstream process may eliminate the need for
the task altogether.
That is the difference between managing the burden and
reducing its source.
The Real Bottleneck May Be Data Quality, Not Staff
Productivity
When a practice experiences recurring billing problems, the
first instinct is often to look at the billing team.
Can staff process claims faster? Should another employee be
hired? Would an AI-powered denial-management tool help?
These are reasonable questions.
But they can begin too far downstream.
Consider a claim rejected because essential information is
missing or inconsistent.
The billing team may have performed its work correctly. The
rejection may originate in an earlier handoff, an incomplete field, conflicting
information across systems or a process that failed to make a requirement
clear.
Giving the billing team a faster tool may help it detect and
resolve the problem. It may not prevent the same issue from appearing again
tomorrow.
This is why healthcare billing should be examined as a data-quality
problem as well as a workflow problem.
The question is not simply whether information exists
somewhere in the organization.
It is whether the right information is accurate, complete,
consistent and available to the right person at the right point in the process.
Information buried in a document, trapped in another system
or discovered only after a claim is rejected may technically exist.
Operationally, it may be almost as useful as information
that does not exist.
The goal should be to make the correct action easier when
information is first captured.
That can involve clearer requirements, better validation,
consistent terminology, defined handoffs and earlier identification of missing
details.
It also means avoiding the temptation to treat every failure
as a staff-performance problem.
If several competent employees repeatedly encounter the same
issue, the process deserves scrutiny before the people do.
A recurring error is not just a recurring task. It may be
evidence that something upstream needs to change.
Artificial Intelligence Cannot Magically Repair a Broken
Process
Artificial intelligence is increasingly being applied to
healthcare administration, including documentation, coding support, claims
operations and revenue-cycle workflows.
Used appropriately, it can be valuable. AI can help identify
patterns, summarize information, flag inconsistencies and reduce repetitive
tasks.
But there is a limitation that deserves more attention:
AI cannot reliably turn missing or incorrect source
information into truth simply by processing it more elegantly.
If the original record is incomplete, an AI system may
identify the gap—or it may produce an output that still requires careful
verification.
If two systems contain conflicting details, the technology
needs a reliable way to identify which information is authoritative.
If staff members use inconsistent definitions, an algorithm
may inherit that inconsistency.
The problem is not that AI is useless.
The problem is expecting intelligence downstream to
compensate indefinitely for poor information upstream.
Imagine asking an exceptionally capable assistant to prepare
a financial report using three spreadsheets that disagree.
The assistant may produce an attractive report in seconds.
That does not make the underlying numbers correct.
Healthcare faces the same principle, with additional stakes
involving privacy, compliance, patient care and reimbursement.
A more sensible sequence is:
- Define
the information required.
- Capture
it as close as practical to its source.
- Validate
it against appropriate rules.
- Identify
missing or conflicting information early.
- Route
exceptions to the right person.
- Use AI
and automation where they genuinely add value.
- Measure
whether the underlying problem actually decreases.
This is not an argument against AI.
It is an argument against asking AI to become a permanent
cleanup crew for a process nobody has redesigned.
Or, to put it less politely: buying a smarter mop is not
always the same as fixing the leaking pipe.
What Physician-Owned Practices Can Do Differently
Small and medium-sized practices often operate with limited
staffing, tight margins and little appetite for another complicated
implementation.
They cannot redesign everything overnight.
They can, however, start with a focused review of recurring
administrative work.
The objective is to distinguish necessary work from work
caused by preventable failure.
Step 1: Identify the recurring problems
Look at the issues that repeatedly consume staff attention.
Examples include incomplete insurance information, missing
documentation, avoidable claim corrections, repeated eligibility checks,
unclear authorization status and information that must be entered into multiple
systems.
Do not begin with the most technologically exciting problem.
Begin with the one that repeatedly wastes time or delays
resolution.
Step 2: Trace each problem backward
When a claim is rejected or a staff member must chase
information, ask:
- What
was missing, incorrect or inconsistent?
- At
what point should the information have been captured?
- Who
had access to it?
- Was
the requirement clear?
- Did
the relevant systems communicate?
- Could
the problem have been detected before the claim was submitted?
The objective is to find the earliest preventable point—not
simply the last person who touched the file.
Step 3: Separate human judgment from repetitive
administration
Some tasks require clinical expertise, negotiation,
interpretation or a conversation with a patient.
Others involve repeatedly transferring information, checking
predictable requirements or tracking a status that should already be visible.
Automate repetitive work where it is safe and appropriate.
Preserve human oversight for exceptions and decisions that require it.
The goal is not to remove people from healthcare.
It is to stop consuming their attention on tasks that do not
need their judgment.
Step 4: Make completeness visible
Staff should not have to discover a missing requirement
weeks after the original encounter.
Where possible, build checks into the point at which
information is created or received. Make it clear what is complete, what is
missing and what needs human review.
A useful system does not merely store information. It helps
people determine whether that information is ready for the next step.
Step 5: Fix the process, not just the individual case
When a problem is corrected, record why it happened.
If the same issue appears repeatedly, review the underlying
process.
A corrected claim is a useful result. A corrected process is
a more durable one.
Step 6: Measure outcomes rather than clicks
Do not judge success solely by how many tasks a system
processes or how quickly it completes a workflow.
Consider tracking:
- First-pass
claim acceptance.
- Rework
and correction rates.
- Time
spent on repetitive follow-up.
- Days
in accounts receivable.
- Frequency
of recurring documentation gaps.
- Time
from identifying a problem to resolving its cause.
- Staff-reported
administrative burden.
Choose metrics that reflect the practice’s priorities and
establish a baseline before introducing a new tool.
A dashboard full of completed tasks can look impressive.
A declining number of unnecessary tasks is more persuasive.
Step 7: Listen to the people closest to the problem
Billing specialists, front-desk staff, medical assistants,
clinicians and patients often experience different parts of the same broken
process.
Ask where information gets lost, which requests repeatedly
arrive too late and what work people perform only because another step failed.
They may already know where the system is leaking time.
The challenge is to convert those observations into
improvements rather than expecting everyone to keep working around the same
defects.
Five Metrics That Tell a More Honest Story
If a practice wants to understand whether administrative
improvement is real, it should look beyond the number of automated
transactions.
First-pass claim acceptance
How often are claims accepted without preventable correction
or resubmission? Define the metric consistently and distinguish
submission-level acceptance from payer adjudication outcomes.
Rework rate
How frequently must staff revisit work because information
was missing, inaccurate or inconsistent? Categorize the causes so the practice
can distinguish preventable errors from legitimate payer or case complexity.
Administrative touches per claim
How many separate staff actions are required to move a claim
through the process? A reduction may indicate a simpler workflow, although
fewer touches alone do not prove better outcomes.
Time to resolution
When an issue occurs, how long does it take to resolve
it—and how much of that time is spent waiting for information, clarifying
responsibility or repeating work?
Recurrence of known errors
After a problem is corrected, does it return? If the same
error keeps appearing, the organization may be treating individual symptoms
without correcting the underlying cause.
These metrics should be interpreted together. No single
measure explains every revenue-cycle problem, and improvements should never
come at the expense of accuracy, compliance or patient access.
The principle is straightforward: measure the friction,
identify its source and verify that the intervention reduces it.
The Patient Experience Is Part of the Administrative
Workflow
Healthcare administration is often discussed as an internal
operational issue.
But the boundaries are not as neat as organizational charts
suggest.
A patient may be asked to repeat information already
provided. A family member may need to clarify an unexplained bill. Someone
recovering from a serious illness may receive confusing communications from
different departments.
Not every such experience is preventable. Healthcare is
complicated, and some cases genuinely require multiple steps.
But administrative friction should not be assumed harmless
simply because it happens outside the examination room.
For patients, confusion can create uncertainty. Repeated
requests can erode confidence. Unclear bills can become a source of anxiety.
For people already dealing with serious health challenges, the burden can feel
disproportionate.
Elkins described how receiving medical bills became
overwhelming during her recovery from a traumatic brain injury. Her account is
an individual experience, not a population-level study. It should not be used
to claim that every patient responds the same way.
Its value is that it makes a familiar administrative process
visible through one person's experience.
A bill is a document to an organization.
To a patient, it may represent uncertainty about money,
coverage, responsibility and what happens next.
The difference matters.
A process can be technically compliant and still be
unnecessarily difficult to navigate. A claim can be operationally complete
while a patient remains confused about the financial outcome.
The goal should be accurate administration and clear
communication—not merely a completed transaction.
The Future of Healthcare AI Should Be Measured by What
Disappears
The next stage of healthcare AI should not be judged solely
by how many administrative tasks it can perform.
It should also be judged by how many unnecessary tasks it
helps prevent.
That requires moving beyond the assumption that every
problem deserves a new workflow.
Sometimes the right intervention is a better integration.
Sometimes it is clearer data standards. Sometimes it is improved training.
Sometimes it is a change to the way information is captured. And sometimes an
AI tool is the right answer.
The important thing is to diagnose the problem before
choosing the technology.
A mature healthcare AI strategy should ask:
- Can we
detect incomplete or inconsistent information earlier?
- Can we
prevent a predictable error before it becomes a denial or delay?
- Can we
reduce repeated data entry without compromising accuracy?
- Can we
make responsibility for the next step clear?
- Can we
identify the root cause of recurring rework?
- Can we
show that the change improved outcomes rather than merely increasing
activity?
This is less glamorous than announcing that AI will automate
an entire department.
It is also more practical.
Healthcare does not need technology for technology’s sake.
It needs systems that make reliable work easier, exceptions more visible and
preventable errors less common.
The most valuable AI may sometimes be the AI that helps a
task disappear—not because it performs the task faster, but because the process
no longer requires the task.
The OnnX Perspective: Move Upstream
This is the operational question behind my work with OnnX.
OnnX is being developed around a premise that deserves
greater attention in healthcare billing: many downstream billing problems
begin as upstream information problems.
Revenue-cycle tools can help practices manage claims,
denials, coding and follow-up. Those functions matter.
But when a problem begins with incomplete, inconsistent or
poorly structured information, the long-term opportunity may be to improve the
information before it enters the downstream workflow.
That means asking different questions.
Not only: How do we process the claim?
But: Was the information ready when the claim was created?
Not only: How do we handle the denial?
But: Could the underlying issue have been identified
earlier?
Not only: How do we make the staff member more productive?
But: Why is that staff member repeatedly doing work that
should not be necessary?
This is not a claim that every denial can be prevented,
every workflow automated or every reimbursement challenge solved by one
platform.
It is a design principle: prevent what can reasonably be
prevented, make exceptions easier to manage and use technology where it solves
a clearly defined problem.
For small and medium-sized physician-owned practices, that
distinction matters. Limited staff should not have to compensate indefinitely
for disconnected systems and recurring information gaps.
The opportunity is not to add technology to every step.
It is to make the whole process require fewer avoidable
steps.
A Human Story, Not a Technology Sales Pitch
Katherine Elkins’s story should not be reduced to a metaphor
for billing software.
She is a nurse who survived a devastating accident, worked
through a difficult recovery and returned to a profession she loves.
Her account also highlights the people who supported her:
the clinicians who treated her, the rehabilitation team, the colleagues who
helped her return and the individuals who offered small acts of kindness when
she needed them.
The Dippin’ Dots mattered. The chance to go outside and
touch the fountain water mattered. The support that helped her return to work
mattered.
These details remind us that healthcare is not merely a
collection of transactions. It is a human relationship carried out through a
complex system.
Technology can help that system work better. But the measure
of success should be whether it reduces unnecessary friction and allows people
to direct more attention toward what matters.
For a patient, that may mean less confusion during recovery.
For a nurse, it may mean more time and energy for patient
care.
For a physician-owned practice, it may mean fewer
preventable corrections, more predictable revenue-cycle operations and less
staff time spent chasing information.
These are connected goals, but they are not interchangeable.
Each requires evidence, thoughtful design and attention to the people affected.
The lesson is not that technology can replace kindness.
It is that good systems should stop making people work so
hard just to navigate the system.
Myth-Buster: “If We Automate It, We’ve Fixed It”
Myth: Administrative automation automatically means
administrative improvement.
Reality: Automation can reduce repetitive work, but
it can also make an inefficient process run faster without correcting the
source of the inefficiency.
Myth: Every denial is a billing department problem.
Reality: Some problems originate earlier in the
process, including missing or inconsistent information, unclear handoffs or
requirements that were not identified in time. Others stem from payer rules,
coverage limitations or factors requiring different interventions.
Myth: More dashboard activity means better
performance.
Reality: Activity is not the same as resolution.
Practices should also measure rework, recurrence, accuracy and outcomes that
matter to patients and staff.
Myth: AI will automatically solve data-quality
problems.
Reality: AI can help identify patterns and flag
inconsistencies, but reliability depends on the information available, system
design and appropriate human oversight.
Myth: The patient’s administrative experience is
separate from care.
Reality: Administrative processes shape how patients
understand bills, respond to requests and navigate the financial side of
healthcare. They deserve attention to clarity and usability.
Frequently Asked Questions
Does Katherine Elkins’s experience prove that medical
billing harms patients?
No. Her account describes the difficulty of managing medical
bills during recovery from a traumatic brain injury. It is an individual story,
not proof of a general causal relationship. It illustrates why administrative
burden deserves attention, particularly when people are vulnerable.
Is automation a bad idea for healthcare practices?
No. Automation can improve consistency, reduce repetitive
work and lower administrative costs. The argument is for choosing automation
strategically and addressing preventable sources of rework rather than assuming
every existing task should simply be automated.
What is upstream data quality in medical billing?
It means capturing and validating relevant information as
close as practical to where it originates, so downstream processes receive
information that is accurate, complete, consistent and usable. Requirements
depend on the workflow, payer rules and clinical context.
Where should a small practice begin?
Start with one recurring problem that consumes staff time or
delays claim resolution. Review a sample of cases, identify common causes,
trace them back to the earliest preventable step and test a focused
improvement. Measure results before expanding the intervention.
How should practices decide whether an AI tool is
worthwhile?
Define the problem first, establish a baseline and specify
the desired outcome. Assess accuracy, integration, privacy, compliance,
usability and the human review required. Then evaluate whether the tool
improves the chosen outcome in actual practice—not merely whether it performs
well in a demonstration.
The Question Healthcare Should Be Asking
Katherine Elkins’s journey from nurse to patient and back to
nursing is a story of resilience, human connection and the long road to
recovery.
It also challenges healthcare leaders to think about the
systems surrounding care.
We cannot eliminate every administrative task. We cannot
prevent every denial. We cannot assume every problem begins with poor data. And
we should never promise that technology will make a complicated healthcare
system frictionless.
But we can be more deliberate about distinguishing necessary
work from work created by preventable failures.
We can make information clearer at the point of capture. We
can improve handoffs. We can examine recurring errors instead of repeatedly
correcting them. We can measure whether automation reduces the burden rather
than merely shifting it to another person.
And we can remember that every administrative workflow
ultimately affects people—patients, families, clinicians and staff.
Here is the contrarian question I would put to every
healthcare technology leader and practice owner:
What if the next big breakthrough in healthcare AI is not
automating more work—but proving how much unnecessary work we can eliminate?
Because the future of healthcare should not be measured by
how efficiently we make people navigate broken processes.
It should be measured by how many of those processes we no
longer need.
And if your latest automation project has made the same
mistake happen 30% faster, congratulations: you have built a very efficient way
to be wrong.
Perhaps the next productivity breakthrough is not another
dashboard.
Perhaps it is fewer reasons to open the dashboard in the
first place.
About the Author
Dr. Daniel Cham is a physician and medical consultant with
expertise in medical tech consulting, healthcare management and medical
billing. He focuses on practical insights for professionals at the intersection
of healthcare and medical practice. As the physician-led founder of OnnX, he is
interested in improving upstream data quality to reduce avoidable billing
friction for physician-owned practices.
Continue the Conversation
What is one recurring administrative task in your
practice that should never have become a task in the first place?
I welcome perspectives from physicians, practice managers,
billers, nurses and healthcare technology leaders. The people doing the work
often know exactly where the system is creating unnecessary friction.
If this perspective resonates, repost it so more healthcare
professionals can join the discussion.
♻️ Repost to challenge the
assumption that automating a task is always the same as improving a process.
· Subscribe and watch on YouTube
References and Further Reading
- LCMC
Health, “From Nurse to Patient and Back: Touro Nurse Returns to Caring
for Patients After Traumatic Brain Injury,” October 16, 2026.
- Madeleine
Aggeler, The Guardian, “‘Don’t sweat the small stuff’: hospice
workers share 12 lessons about life and death,” October 21, 2026. The
opening quote is from Courtney, a hospice nurse practitioner in Missouri
who asked that her surname be withheld.
- CAQH,
“2025 CAQH Index Shows U.S. Healthcare Avoided $258 Billion and
Accelerated Automation, Interoperability and AI Adoption,” February 19,
2026.
Disclaimer
This article is for educational and general informational
purposes only. It does not constitute medical, legal, coding, reimbursement or
financial advice. The human-interest narrative is based on publicly reported
information; broader operational recommendations are commentary and should be
adapted to each practice’s circumstances. Verify current payer requirements,
applicable laws and professional guidance before making clinical, billing or
compliance decisions.
#Healthcare #MedicalBilling #HealthcareAI
#RevenueCycleManagement #PhysicianEntrepreneur #HealthcareInnovation
#PracticeManagement #HealthTech #DataQuality #PatientExperience
#AdministrativeBurden #ArtificialIntelligence
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