Kev Rands' “fantastic, mundane” life raises a bigger question: If healthcare is supposed to give people their lives back, why are we using AI to create more activity instead of removing the work that gets in the way?
“We will have to decide not only which parts of medicine
can be automated, but which parts we want to automate.” — John
Whyte, MD, MPH, CEO, American Medical Association, STAT,
September 9, 2026
The most extraordinary thing about a heart transplant may
be what happens when the patient no longer needs to think about the heart
transplant.
Kev Rands was just 10 days old when he underwent his
first open-heart surgery.
His mother noticed he wasn't feeding properly.
She called the midwife.
The midwife called the GP.
The GP sent him to hospital.
And suddenly, a newborn baby's life became a medical
emergency.
Rands had a rare congenital heart condition called double
inlet left ventricle.
Doctors operated.
Then they operated again.
Throughout his childhood and adolescence, he underwent six
open-heart surgeries, along with numerous other procedures involving coils,
stents, pacemakers and heart valves.
Hospital became part of childhood.
At school, he was frequently absent.
Sometimes he spent five or six weeks in hospital.
His medication made him feel unwell.
He developed blood clots.
His baby teeth turned black and fell out.
It was, by almost any definition, an extraordinary medical
life.
But eventually the doctors reached the point where another
repair wasn't enough.
At 23, Rands was placed on the heart-transplant
waiting list.
Then he waited.
For five years.
There were phone calls.
A heart might be available.
He would prepare.
Then another call:
The cross-match wasn't right.
The heart wasn't suitable.
Try again.
Eventually, after two years of this emotional roller
coaster, he asked to come off the transplant list.
“It was all too much.”
Then his condition deteriorated.
He went back on the list.
At 27, the call finally came.
Rands received the heart of an unknown young man.
The donor's name has not been publicly disclosed.
The operation took place at Freeman Hospital in Newcastle,
after hospitals in London, Bristol and Leeds reportedly declined his case
because of its complexity.
And then something remarkable happened.
Nothing.
No more dramatic medical plot twist.
No blockbuster ending.
No miracle montage.
Instead, Kev Rands got to live.
Fourteen years later, he is 41 years old, married,
and the father of an 11-year-old daughter and a 4-year-old son.
He works as a van salesman.
He goes to the gym.
He takes family photographs.
He has birthdays, errands, shopping trips and ordinary
mornings.
And he describes his life with two words that seem almost
hilariously understated after everything he has survived:
“Fantastic, mundane.”
Think about that.
Six open-heart surgeries.
Years on a transplant waiting list.
Cancelled transplant calls.
A complex operation involving a stranger's heart.
And the reward?
A boring Tuesday.
A family dinner.
A trip to the gym.
A child's birthday.
A normal life.
Mundane.
And perhaps that is the most profound thing about the story.
Maybe Healthcare Has Been Measuring the Wrong Thing
Healthcare loves extraordinary outcomes.
We celebrate the surgery.
The breakthrough drug.
The new device.
The transplant.
The AI model.
The number of patients treated.
The number of claims processed.
The number of denials overturned.
The number of notes generated.
The number of tasks completed.
We love numbers.
Numbers make excellent dashboards.
They also make excellent hiding places.
Because patients don't necessarily want more healthcare.
They want more life.
Kev Rands didn't need a heart transplant so he could become
an excellent long-term heart-transplant patient.
He needed one so he could stop being a patient.
That distinction matters.
The best outcome of healthcare may sometimes be the moment
healthcare becomes irrelevant to someone's everyday life.
A successful transplant eventually becomes:
“Dad is going to the gym.”
A successful cancer treatment eventually becomes:
“Mom is going to work.”
A successful pediatric intervention eventually becomes:
“She's going to school.”
The medical intervention disappears into the background.
Life takes over.
That may be the real definition of success.
And it raises a rather uncomfortable question for healthcare
technology:
What if technology should be judged by what it allows
people to stop doing?
We Have an Odd Obsession With Making Healthcare More
Efficient
Healthcare has spent decades trying to make things faster.
Faster documentation.
Faster coding.
Faster claims.
Faster authorizations.
Faster scheduling.
Faster eligibility checks.
Faster appeals.
Faster communication.
Now we have AI to make many of those things even faster.
Wonderful.
But faster is not automatically better.
If I give you a faster way to dig a hole you don't need,
congratulations.
You are now an extremely efficient hole digger.
Healthcare may occasionally resemble this.
We build a workflow.
The workflow creates administrative work.
Then we build software to automate the administrative work.
Then we add AI to automate the software.
Then we create a dashboard to measure how efficiently the AI
automated the software that automated the work created by the workflow.
At some point, someone should probably ask:
Why are we digging the hole?
The New Question for AI
This is why a recent comment from John Whyte, MD, MPH,
CEO of the American Medical Association, caught my attention.
Writing in STAT this month about AI and medicine,
Whyte made an important distinction:
“We should not confuse the automation of medical tasks
with the automation of medicine itself.”
That's a much more interesting AI conversation.
Because the question isn't simply:
What can AI automate?
AI can automate a lot.
The better question is:
What should we automate?
And I would add a third:
What should we eliminate altogether?
Those are three very different questions.
Automation Is Not the Same as Improvement
Imagine a physician practice with a recurring problem.
A claim keeps getting rejected because some piece of
information is missing or inconsistent.
The traditional response is:
- Find
the denial.
- Identify
the problem.
- Contact
someone.
- Correct
the information.
- Resubmit.
- Monitor.
- Repeat.
Now introduce AI.
AI can potentially:
- Find
the denial.
- Identify
the problem.
- Draft
the correction.
- Generate
the appeal.
- Route
the task.
- Monitor
the status.
- Repeat.
That sounds impressive.
And it may be useful.
But here's the annoying question:
Why did the problem reach the claim in the first place?
We have automated the repair.
We haven't necessarily fixed the cause.
That's the difference between automating a broken
workflow and redesigning the system that created the workflow.
Healthcare Has an Activity Addiction
Look at the language we use.
Claim submitted.
Authorization requested.
Appeal filed.
Task assigned.
Message sent.
Chart closed.
Account worked.
Case escalated.
Everything sounds busy.
Very little tells us whether anything actually happened.
A submitted claim is not payment.
An authorization request is not authorization.
A referral is not an appointment.
An appeal is not a successful appeal.
A message is not communication until someone receives it and
acts on it.
A task marked “complete” is not necessarily a problem
solved.
Healthcare has developed a strange substitute for progress:
activity that looks like progress.
And AI is extraordinarily good at producing activity.
The Billion-Dollar Question May Be: What Work Should
Disappear?
Recent healthcare technology discussions are increasingly
moving in this direction.
A September 25 HealthLeaders analysis reported that
healthcare leaders looking at AI and clinician burden emphasized workflow
redesign alongside technology, rather than treating technology alone as the
solution.
That distinction is crucial.
Because adding technology to a bad workflow can simply give
the bad workflow a faster engine.
The real opportunity is different:
Use technology to make the bad workflow unnecessary.
That is much harder.
It is also much more valuable.
The Most Expensive Software in Healthcare Might Be a
Person
Consider what happens inside a physician-owned practice.
A patient's information is incomplete.
Someone notices.
A medical assistant sends a message.
The physician responds.
The scheduler checks something.
The billing team discovers another problem.
Someone logs into a payer portal.
Someone makes a phone call.
Someone waits on hold.
Someone documents the phone call.
Someone follows up.
Someone checks again.
Someone asks:
“Did anyone ever hear back?”
This is not a software stack.
This is a human stack.
And humans are incredibly expensive middleware.
We don't usually call them middleware, of course.
We call them:
“Staff.”
“Care coordinators.”
“Practice managers.”
“Billing specialists.”
“Administrative support.”
And they are often extraordinarily good at compensating for
broken processes.
That is exactly why broken processes survive.
Humans are too good at rescuing them.
The Healthcare System's Secret Superpower Is Human
Compensation
Think about how many healthcare workflows function because
somebody remembers.
Someone remembers to call the patient.
Someone remembers to check eligibility.
Someone remembers the authorization expires Friday.
Someone notices the payer changed.
Someone recognizes that the diagnosis doesn't match the
procedure.
Someone catches the missing documentation.
Someone says:
“Wait. That doesn't look right.”
That human vigilance saves healthcare every day.
But it is not scalable infrastructure.
If the process depends on one exceptionally attentive person
catching the same error 400 times a month, the organization hasn't solved the
problem.
It has hired a hero.
Heroes are wonderful.
Systems are better.
Most of the Problem Starts Upstream
This is where the medical billing problem becomes more
interesting.
We tend to treat billing as something that happens after
care.
The patient is seen.
The claim is created.
The payer responds.
Then the revenue cycle team goes to work.
But the billing problem often begins much earlier.
At intake.
During scheduling.
At registration.
During documentation.
When insurance information changes.
When an authorization is requested.
When clinical and operational information don't line up.
The downstream claim is simply where the problem becomes
visible.
That is different.
The claim may be the messenger.
And we have a habit of shooting the messenger.
Healthcare Billing Is a Data Problem Before It Is a
Workflow Problem
This is the central idea behind OnnX:
Healthcare billing is a data-quality problem, not simply
a tooling problem.
The industry has become extremely sophisticated at managing
downstream uncertainty.
Denial management.
A/R management.
Appeal management.
Claim scrubbing.
Coding review.
Payment reconciliation.
All valuable.
But what if we spend less time asking:
“How do we process the problem?”
and more time asking:
“Why did the problem exist?”
That is a different operating philosophy.
Instead of:
More downstream intervention.
Think:
Better upstream structure.
Instead of:
More follow-up.
Think:
Fewer reasons to follow up.
Instead of:
More automation.
Think:
Less work.
The Difference Between “Submitted” and “Solved”
This may sound like semantics.
It isn't.
Suppose a clinic submits 10,000 claims.
That's impressive.
Unless 1,500 require intervention.
Then the real question becomes:
What happened to those 1,500?
Suppose the practice processes 2,000 denials.
Great.
Unless 1,300 were caused by the same three recurring
problems.
Then the achievement isn't really “2,000 denials managed.”
It may be:
“We discovered three problems we haven't eliminated.”
That's a completely different way of looking at revenue
cycle management.
And potentially a much more productive one.
The Prior Authorization Machine
Prior authorization makes this especially visible.
The American Medical Association's 2025 physician survey
reported that physicians complete an average of 40 prior authorizations per
week, consuming about 13 hours of physician and staff time weekly.
The survey also found that 94% of physicians said prior authorization
contributes to burnout, while 95% said it delays access to necessary
care.
The obvious response is:
“Let's build better AI for prior authorization.”
And perhaps we should.
But the deeper question is:
Why does the healthcare system require so much human
labor to move information that often already exists somewhere inside the
healthcare ecosystem?
The answer is often fragmentation.
The information exists.
But it may be sitting in different systems.
Different formats.
Different fields.
Different workflows.
Different people's heads.
So humans become the bridge.
Again.
AI Can Be the Bridge—or It Can Become Another Toll Booth
AI can absolutely reduce administrative burden.
But AI itself is not the strategy.
That's an important distinction.
We could deploy AI everywhere and still have a terrible
healthcare system.
Why?
Because AI can optimize a bad process.
It can also create new work.
New alerts.
New outputs.
New exceptions.
New things to review.
New dashboards.
New “AI-generated recommendations” someone must approve.
Suddenly we have:
AI creating work for humans to supervise the AI that was
supposed to reduce human work.
That would be a magnificent joke if it weren't happening.
“Human in the Loop” Shouldn't Mean “Human Doing
Everything”
There is a reasonable argument for human oversight.
Healthcare involves risk.
Clinical judgment matters.
Patients are not spreadsheets.
AI should not casually make consequential decisions without
appropriate oversight.
John Whyte makes this point in his recent STAT essay:
AI can help physicians search information, document care and reduce
administrative work, but medicine still requires judgment, relationships,
values and human responsibility.
But there is a difference between:
human oversight
and
human participation in every tiny administrative step.
A physician should review a clinically meaningful
recommendation.
A physician should not necessarily be required to rescue a
missing demographic field.
A practice manager should oversee operations.
They should not spend Friday afternoon hunting through payer
portals for information that should have been structured automatically.
A biller should exercise judgment.
They should not spend half the day repeatedly correcting the
same preventable error.
Human-in-the-loop should mean:
humans handle what requires humans.
Not:
humans remain trapped inside every workflow because
nobody redesigned it.
What If We Stopped Asking “What Can AI Do?”
Try this instead.
Question 1:
What work is genuinely necessary?
Question 2:
What work exists because the system is poorly designed?
Question 3:
What work exists because information is incomplete?
Question 4:
What work exists because of unnecessary handoffs?
Question 5:
What work should be automated?
Question 6:
What work should simply disappear?
That last question is the one healthcare technology doesn't
ask often enough.
The Handoff Tax
Every handoff introduces risk.
Patient → front desk.
Front desk → clinical team.
Clinical team → authorization team.
Authorization team → payer.
Payer → practice.
Practice → billing.
Billing → payer.
Payer → billing.
Billing → physician.
Physician → staff.
Staff → patient.
At every transition, information can be:
- lost
- delayed
- reinterpreted
- duplicated
- mistyped
- misunderstood
- forgotten
We then hire people to coordinate the handoffs.
Then we buy software to coordinate the coordinators.
Then we add AI to coordinate the software.
At some point, maybe the boldest innovation isn't better
coordination.
Maybe it is:
fewer handoffs.
A Referral Is Not Treatment
A referral is not treatment.
An authorization request is not approval.
A submitted claim is not payment.
A denial appeal is not resolution.
A message sent is not a completed communication.
A task assigned is not a completed task.
This distinction sounds obvious.
Yet healthcare workflows routinely measure the first thing
because it is easier to measure.
Activity is visible.
Completion is harder.
The future of healthcare operations should move toward
measuring what actually matters.
Not:
“How many tasks did we touch?”
But:
“How many problems disappeared?”
The Real ROI Isn't Always Dollars
Healthcare executives understandably ask:
How much money did the technology save?
Good question.
But there is another question.
How much human attention did it return?
Suppose technology saves a practice 100 administrative hours
per month.
Where did those hours go?
If they simply became 100 more hours of administrative work,
the system hasn't created much human freedom.
If they become:
- more
patient time
- fewer
late evenings
- better
staff retention
- fewer
interruptions
- faster
resolution
- more
time for practice improvement
- less
physician inbox time
then the return is different.
It is operational.
Financial.
And human.
What Should Physicians Get Back?
This is the question I care about most.
Not:
How much more can physicians do?
But:
What should physicians no longer have to do?
Should a physician have to chase documentation because a
downstream process can't determine what's missing?
Should a physician have to repeatedly answer administrative
questions caused by disconnected systems?
Should a practice owner personally intervene because staff
cannot see where a claim stalled?
Should highly trained clinicians spend their attention on
work that a well-designed system could prevent?
The answer isn't “automate everything.”
The answer is:
protect human attention.
Because attention is one of the scarcest resources in
healthcare.
And unlike software licenses, you cannot renew it.
The Goal Isn't to Replace Humans
This matters.
The interesting future isn't:
AI replaces the biller.
It is:
AI removes the repetitive work that prevents the biller
from using judgment.
It isn't:
AI replaces the physician.
It is:
AI removes administrative noise so the physician can
spend more attention on the patient.
It isn't:
AI eliminates staff.
It is:
AI eliminates unnecessary work.
That distinction changes everything.
The goal isn't fewer humans.
The goal is fewer humans doing work that machines should
have prevented them from needing to do.
Kev Rands Gives Us the Best Test
Here's the test I'd apply to almost every healthcare
technology:
What does the human get back?
Kev Rands received a donor heart.
What did he get back?
Not another medical procedure.
Not another complicated care pathway.
He got:
A wife.
Two children.
A gym.
A job.
Family photographs.
Ordinary mornings.
Ordinary problems.
Ordinary life.
He got to become something more interesting than a patient.
He got to become Kev.
And that is why his phrase sticks:
“Fantastic, mundane.”
The mundane wasn't the consolation prize.
It was the outcome.
Maybe “Boring” Is the Future of Healthcare
Healthcare technology companies love words like:
Transformative.
Revolutionary.
Disruptive.
Autonomous.
Generative.
Predictive.
Intelligent.
But the best technology may produce none of those feelings.
It may feel boring.
The authorization is already there.
The insurance information is correct.
The claim doesn't need fixing.
The staff member doesn't need to call.
The physician doesn't receive another notification.
The patient doesn't have to explain the same thing twice.
The account gets paid.
Nobody celebrates.
Nobody writes a press release.
Nobody posts a LinkedIn announcement.
It just worked.
That may be the highest compliment healthcare technology can
receive.
The Best AI May Be the AI Nobody Talks About
We tend to evaluate AI by what it produces.
A note.
A summary.
A prediction.
A recommendation.
A claim.
An appeal.
But perhaps we should also evaluate AI by what it prevents.
A phone call.
A denial.
A duplicate entry.
A missing field.
A needless handoff.
A repeated question.
A physician interruption.
A staff escalation.
A patient delay.
That changes the scoreboard.
Instead of:
“Look how much AI did.”
We could say:
“Look how much unnecessary work disappeared.”
That is much harder to market.
It may also be much more valuable.
The Future of Medical Billing Might Be Less Billing
That sounds ridiculous.
It may actually be the point.
If we improve the quality of information at the source,
fewer claims should require intervention.
If we reduce ambiguity, fewer people should need to clarify
it.
If we reduce handoffs, fewer things should get lost.
If we improve consistency, fewer exceptions should occur.
If we eliminate preventable problems, fewer staff hours
should be spent fixing them.
So perhaps the future of revenue cycle management isn't:
more sophisticated billing.
Perhaps it is:
less unnecessary billing work.
The best revenue-cycle department may not be the one that
handles the most problems.
It may be the one that creates the fewest problems for
itself.
A Simple Practice Audit
You can test this tomorrow without buying anything.
Ask your team five questions.
1. What do you repeatedly fix?
Not occasionally.
Repeatedly.
2. What information do you repeatedly chase?
If the answer is “the same information,” you've probably
found an upstream problem.
3. Where do the most handoffs occur?
Map them.
Don't assume they're necessary.
4. Which tasks are “completed” but not actually resolved?
This is where activity metrics become dangerous.
5. If we could permanently delete one administrative
task, what would it be?
Don't ask leadership first.
Ask the people doing the work.
They know where the bodies are buried.
Then Ask the More Interesting Question
Once you've identified the annoying task, don't immediately
ask:
“Which software can automate it?”
Ask:
“Why does this task exist?”
Then:
“What information would have prevented it?”
Then:
“Where should that information have been captured?”
Then:
“Why wasn't it?”
That takes you upstream.
And once you're upstream, you're no longer simply doing
workflow optimization.
You're doing system design.
Healthcare Doesn't Need More Digital Busywork
The industry already has enough software.
Enough dashboards.
Enough alerts.
Enough portals.
Enough passwords.
Enough notifications.
Enough “action required” messages.
Enough systems that promise to simplify healthcare while
adding another login.
The next generation of healthcare technology should have a
more difficult mission:
remove complexity rather than decorate it.
Don't make the maze prettier.
Make the maze smaller.
Don't make the paperwork faster.
Ask why the paperwork exists.
Don't make physicians better at administrative work.
Give administrative work less access to physicians.
Don't automate every problem.
Prevent more problems from becoming problems.
That Is the Difference Between Automation and Design
Automation asks:
“How can we make this task faster?”
Design asks:
“Why are we doing this task?”
Automation improves the process.
Design questions the process.
Automation can save minutes.
Design can eliminate the need for the minutes.
That distinction is easy to miss because automation produces
impressive demos.
Design produces fewer things to demo.
And fewer things happening is sometimes the whole point.
The Strange Lesson of an Anonymous Heart
There is something deeply human about Kev Rands' story.
He doesn't know the donor's name.
The donor's family doesn't appear to know him personally.
There is no neat Hollywood ending.
No reunion.
No handshake.
No photograph of the two families together.
There is simply a decision made by strangers.
Someone said yes.
And years later, another family is living because of it.
Rands has sent photographs to the donor's relatives through
the NHS.
He hasn't received a response.
Still, he wanted them to know:
“I didn't waste it.”
That may be one of the most powerful measures of healthcare.
Not how much happened.
But what happened because someone cared enough to make
the right thing possible.
The Ultimate Healthcare Metric Might Be Life Outside
Healthcare
Maybe we have been looking at the healthcare system from the
wrong direction.
We measure:
hospitalizations.
visits.
procedures.
claims.
codes.
denials.
payments.
workflows.
tasks.
Maybe we should also measure something much simpler:
How much life happens outside the system because the
system worked?
For Kev Rands, that life is a family.
For a physician, it might be dinner at home.
For a nurse, it might be leaving on time.
For a practice manager, it might be one afternoon without a
fire.
For a patient, it might be never having to call the billing
office.
These aren't small outcomes.
They're the reason the system exists.
The Contrarian Future
The healthcare industry is asking:
How can AI help us do more?
I think there is a better question:
How can AI help us need less?
Less rework.
Less chasing.
Less duplication.
Less manual reconciliation.
Less administrative noise.
Less handoff.
Less uncertainty.
Less time spent fixing predictable problems.
Less time spent asking:
“Who was supposed to do this?”
And more time doing what only humans can do.
Care.
Judgment.
Conversation.
Empathy.
Relationships.
Life.
Final Thoughts
1. Stop confusing activity with outcomes.
A submitted claim is not revenue. A requested authorization
is not approval. A closed task is not necessarily a solved problem. Measure
completion, not motion.
2. Move upstream.
When the same problem repeatedly appears downstream, don't
merely automate the repair. Find where the information first became unreliable
and fix the source.
3. Protect human attention.
The purpose of healthcare AI should not be to make
physicians, staff and administrators capable of doing an infinite amount of
administrative work.
It should be to make that work require less of them.
Kev Rands received a stranger's heart.
Fourteen years later, his greatest achievement isn't another
medical milestone.
It's that he has an ordinary life.
A wife.
Two children.
A gym.
A job.
A Tuesday.
He calls it:
“Fantastic, mundane.”
Maybe that is what healthcare should aspire to more often.
Not extraordinary healthcare experiences.
Not endless healthcare activity.
Not more technology for technology's sake.
But a system that quietly works well enough that people can
get back to the thing they came for in the first place:
their lives.
And perhaps the best healthcare technology isn't the
technology that makes healthcare more impressive.
It's the technology that makes healthcare less necessary
in the moments when it doesn't need to be there.
One Question for Physicians, Practice Owners and
Healthcare Operators
What is one administrative task your team performs every
week that should probably not exist at all?
Not the task you'd like to automate.
The task you'd like to delete.
What is it?
Why does it exist?
And what would have to change upstream for it to disappear?
Tell me in the comments.
Then share this with the person in your organization who
spends too much of their day fixing problems that should have been prevented
upstream.
Because perhaps the next healthcare breakthrough isn't
another thing we can automate.
Perhaps it's something we no longer need to do.
About the Author
Dr. Daniel Cham is a physician and medical consultant
with expertise in medical technology consulting, healthcare management, and
medical billing. He focuses on practical insights that help healthcare
professionals navigate complex challenges at the intersection of healthcare,
technology and medical practice.
Connect with Dr. Cham on LinkedIn to
learn more.
PS: A free resource is available in the Featured section
of my LinkedIn profile—no signup needed.
Continue the Conversation
Healthcare is changing quickly. But the most important
question isn't simply what technology comes next.
It's what that technology makes possible for patients,
physicians and healthcare teams.
Knowledge drives progress. Start your journey here.
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Sources & Further Reading
1. NHS
Blood and Transplant — Kev Rands' story
The official NHS account of Kev Rands, including his rare congenital heart
condition, six open-heart surgeries, first surgery at 10 days old, transplant,
and life after receiving an anonymous donor heart.
NHS Blood and Transplant: Kev Rands' story
2. John Whyte, MD, MPH — STAT
The AMA CEO's September 9, 2026 essay examining AI in medicine and the
distinction between automating medical tasks and replacing the broader practice
of medicine.
STAT: “AI can master medical tasks. Practicing medicine is much
more than that”
3. American
Medical Association — Prior Authorization Survey
The AMA's 2026 report on its 2025 physician survey, including the findings that
physicians complete an average of 40 prior authorizations per week, that
the process consumes about 13 hours of physician and staff time weekly,
and that 94% say prior authorization contributes to burnout.
AMA: Prior authorization reform survey
4. HealthLeaders
— Clinician burden and workflow redesign
A September 25, 2026 analysis examining why technology alone may not reduce
clinician burden and highlighting the importance of workflow redesign and
clinician involvement in AI implementation.
HealthLeaders: “Is the Answer to Reducing Clinician Burden Just
Technology?”
5. NHS
Blood and Transplant — Heart transplantation
Official NHS information on heart transplantation, including the transplant
process, waiting, donor matching and life after transplantation.
NHS
Blood and Transplant: Heart transplantation
Disclaimer
This article is intended for educational and
informational purposes only. It does not constitute medical, legal, financial,
coding, billing, compliance or other professional advice. Healthcare
regulations, payer requirements and reimbursement policies vary and change over
time. Readers should consult appropriate qualified professionals and
authoritative sources when making clinical, operational, financial or business
decisions.
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