Behind every claim, denial, authorization, and clinical note is a person waiting for something that matters. Dáithí's story reminds us who all that work is really for.
“AI has enormous potential in healthcare, but it cannot
replace physician judgment.”
— John
Whyte, MD, MPH, CEO of the American Medical Association
A nine-year-old boy is playing football in his front garden
in Ballymurphy, west Belfast.
His name is Dáithí Mac Gabhann.
A few months ago, that ordinary scene would have been almost
unimaginable.
Dáithí was born with hypoplastic left heart syndrome,
a serious congenital heart condition. He had been waiting for a heart
transplant since 2018.
Eight years.
Think about that.
Eight years is almost an entire childhood.
While other children were worrying about homework, birthday
parties and football practice, Dáithí and his parents, Máirtín Mac Gabhann
and Seph Mac Gabhann, were waiting for a telephone call that could change
everything.
The call finally came.
In July 2026, Dáithí received a heart transplant at Freeman
Hospital in Newcastle upon Tyne.
Weeks later, he came home to Belfast.
And there he was.
Playing football.
His brother Cairbre was there too.
His family was finally able to imagine something that sounds
almost embarrassingly ordinary:
A childhood.
The Irish News reported that Dáithí's return home marked the
end of an almost eight-year journey. His father expressed gratitude to the
donor family, whose identity remains private.
Here is the part that stayed with me.
Dáithí did not spend eight years waiting for better
healthcare technology.
He did not spend eight years waiting for a new AI platform.
He did not spend eight years waiting for a more efficient
revenue cycle.
He was waiting for a chance to live.
And that raises an uncomfortable question for everyone
working in healthcare:
Why have we become so good at measuring healthcare that
we sometimes forget what healthcare is actually for?
The controversial idea
Here is my contrarian view:
Medical billing is not the most important thing happening
in a medical practice.
Obviously.
But it may be one of the most important things determining
whether that practice can continue doing the most important thing.
That distinction matters.
Physicians sometimes talk about billing as if it were an
annoying parasite attached to medicine.
I understand the feeling.
You went to medical school.
You trained for years.
You learned anatomy, physiology, pathology, pharmacology and
clinical reasoning.
You did residency.
You took call.
You managed emergencies.
You learned how to make difficult decisions when the
information was incomplete.
Nobody told you that one day you would spend part of your
career trying to understand why a payer rejected a claim because of a modifier.
Welcome to modern medicine.
We somehow built a healthcare system in which a physician
can save a patient's life in the morning and spend the afternoon arguing with a
payer portal.
That is not a joke.
It is a design failure.
And we should stop pretending it is normal.
The billing department is not the problem
This may sound strange coming from someone building a
medical billing company.
But I don't think the billing department is the problem.
The architecture is the problem.
We have separated clinical care from administrative
infrastructure as if they were unrelated activities.
They aren't.
A physician sees a patient.
A clinical story is created.
That story becomes documentation.
The documentation becomes structured data.
The structured data becomes codes.
The codes become a claim.
The claim becomes a financial transaction.
The payer evaluates it.
Payment comes back.
Or it doesn't.
That is one chain.
We have simply given different parts of the chain different
names.
Clinical.
Administrative.
Financial.
Operational.
Revenue cycle.
But the patient experiences only one thing:
healthcare.
The Dáithí test
I have started thinking about a simple test for healthcare
technology.
I call it the Dáithí test.
Before we celebrate a new healthcare technology, ask:
Does this ultimately help someone get back to living?
Not every technology needs to improve survival.
Some technologies reduce errors.
Some reduce costs.
Some improve access.
Some help clinicians work faster.
All of those can matter.
But somewhere downstream, the purpose should connect to a
human outcome.
For Dáithí, the outcome was wonderfully ordinary.
Walking.
Running.
Playing football.
Being with friends.
Going home.
That is the destination.
The paperwork is the road.
We should not confuse the road with the destination.
Healthcare has a strange addiction to paperwork
Let's be honest.
Healthcare has an almost supernatural ability to turn simple
things into complicated workflows.
A patient needs care.
Someone asks for authorization.
Someone submits a form.
Someone calls.
Someone waits.
Someone sends records.
Someone asks for more records.
Someone resubmits.
Someone receives a denial.
Someone appeals.
Someone waits again.
Eventually someone says:
“Good news. It was approved.”
And everyone celebrates.
Why?
Because we successfully completed a process we designed
ourselves.
That deserves some reflection.
We should not measure our brilliance by how efficiently we
navigate unnecessary complexity.
We should measure our brilliance by how much unnecessary
complexity we eliminate.
That is a very different philosophy.
The numbers are not funny
The humor disappears quickly when you look at the data.
The American Medical Association's 2026 physician survey
found that:
95% of physicians said prior authorization delays
necessary care.
79% said patients sometimes abandon treatment because
of authorization challenges.
92% said prior authorization negatively affects
clinical outcomes.
26% reported that prior authorization had contributed
to a serious adverse event.
Physicians and staff reported completing an average of 40
prior authorizations per physician per week, consuming about 13 hours of
physician and staff time each week.
And 94% said prior authorization contributes to
burnout.
Forty authorizations.
Thirteen hours.
Every week.
For one physician.
That is not an administrative inconvenience.
That is an operating model.
And if your practice is paying someone to spend 13 hours
every week fighting a system, that cost does not disappear.
Someone pays for it.
The practice.
The physician.
The staff.
The patient.
Or eventually the healthcare system.
Here is the part people get wrong about AI
The answer is not:
“Let's put AI on it.”
That sentence should make every physician slightly nervous.
Because AI can automate a bad process.
It can automate an inaccurate process.
It can automate an inefficient process.
It can automate a workflow that nobody should have designed
in the first place.
And now the bad process happens faster.
Congratulations.
We invented the world's fastest bureaucratic machine.
That is not innovation.
It is automation theater.
AI should not make bad billing faster
This is one of the strongest beliefs behind my work with OnnX.
I am not interested in AI simply because AI is popular.
I am interested in whether intelligence can be moved to the right
point in the workflow.
That distinction is critical.
Suppose a claim is denied because information was missing.
Traditional thinking asks:
How can we process the denial faster?
Better thinking asks:
Why didn't we identify the problem before the claim was
submitted?
That is the difference between downstream repair and
upstream prevention.
And I believe healthcare has an enormous opportunity here.
Precision at the source
I call the concept precision at the source.
The basic idea:
The earlier you identify a predictable problem, the
cheaper and easier it usually is to fix.
If a documentation problem is discovered after a denial,
someone has to investigate.
If it is discovered before submission, the fix may take
seconds.
If an authorization problem is discovered after a procedure,
the situation can become painful.
If it is identified before the appointment, the practice has
options.
If a coding inconsistency is identified after payment is
delayed, staff must chase it.
If it is identified before submission, the problem may never
leave the building.
This is not revolutionary technology.
It is basic systems thinking.
Yet healthcare often does the opposite.
We wait for the failure.
Then we build a department to manage the failure.
Then we build software to manage the department.
Then we build AI to manage the software.
At some point, we should probably ask:
What if we just prevented the failure?
A physician's day is not an API
Here is another contrarian thought.
Healthcare technology companies sometimes talk about
physicians as if they were APIs.
Input.
Process.
Output.
Patient enters.
Documentation generated.
Code assigned.
Claim submitted.
Revenue collected.
Beautiful.
Except humans don't work that way.
Physicians are constantly making judgments.
Patients change their stories.
Clinical situations are messy.
Documentation varies.
Payers change rules.
Exceptions happen.
And sometimes the most important information is not the
information that fits neatly into a database.
That is why healthcare AI needs humility.
The system should know when it knows.
And it should know when it does not.
The goal is not autonomous medicine
I am particularly skeptical of the phrase:
“Fully autonomous healthcare.”
Maybe someday.
But today?
I would rather have appropriately supervised intelligence
than impressive autonomy.
In medical billing, AI should help with:
Pattern recognition.
Documentation support.
Coding assistance.
Claim validation.
Payer-rule interpretation.
Denial analysis.
Workflow prioritization.
Exception detection.
Appeal preparation.
But when the consequences become significant, humans should
remain appropriately involved.
That is not a weakness of AI.
It is good system design.
The physician does not need another dashboard
Please.
No more dashboards just because dashboards are easy to
build.
A physician-owner does not wake up thinking:
“I wish I had three more colorful graphs.”
They want answers.
Why is revenue down?
Why are denials increasing?
Which payer is causing the problem?
Why is this service line underperforming?
How many staff hours are being wasted?
Which claims require attention?
What should we fix first?
A good system should answer those questions.
A great system may answer them before the physician asks.
What physicians actually want
Talk to physicians long enough and you discover something
interesting.
They usually don't ask for more technology.
They ask for less friction.
They want:
Fewer phone calls.
Fewer portals.
Fewer denials.
Fewer surprises.
Fewer repetitive tasks.
Less documentation after hours.
Less chasing.
More visibility.
More control.
More time with patients.
That is the product brief.
Everything else is implementation detail.
The biggest billing mistake I see
It is not bad coding.
It is not slow claims.
It is not even denials.
It is finding problems too late.
Think about it.
A practice discovers an error after the claim is rejected.
Why?
Because that is where the system finally became smart enough
to notice.
That is backwards.
The revenue cycle should be increasingly intelligent before
the claim reaches the payer.
Not because we want to manipulate the payer.
Because we want the claim to accurately represent the care
that actually occurred.
That is an important ethical distinction.
The objective is not “get the claim paid”
This may be the most provocative statement in the article:
Getting every claim paid is not the goal.
Accurate payment for legitimate care is the goal.
Those are not identical.
If a claim is unsupported, the right answer is not to find a
clever way around the payer.
If documentation does not support a code, the answer is not
aggressive automation.
If a service was not medically necessary, technology should
not manufacture justification.
The goal is accuracy.
Accuracy protects the patient.
Accuracy protects the physician.
Accuracy protects the practice.
Accuracy protects the healthcare system.
Three experts. Three lessons.
1. William Osler: remember the human being
Osler's philosophy remains useful because it forces us back
toward the patient.
The disease is not the person.
The code is not the patient.
The claim is not the patient.
The patient is the patient.
That sounds obvious.
Healthcare needs reminding.
2. The AMA: administrative burden is becoming clinical
burden
The AMA's latest survey demonstrates that prior
authorization is consuming substantial physician and staff time while
physicians report delays, treatment abandonment and adverse events.
The lesson is not simply:
“Insurance companies are bad.”
That is too easy.
The deeper lesson is:
Administrative friction can become a clinical variable.
That means physician leaders should start treating
administrative performance as part of operational quality.
3. CMS: the future is increasingly structured
CMS has been moving toward greater electronic prior
authorization, interoperability and more specific explanations for certain
denials.
Under CMS's prior authorization rule, impacted payers are
required to provide specific reasons for certain denied requests and meet
defined decision timeframes.
That matters.
Because the future of healthcare administration is becoming
more structured.
The opportunity is not simply to digitize paper.
It is to make structured information useful.
The hidden opportunity for independent practices
Large health systems have scale.
Independent practices have something else:
clarity.
When a practice has 12 employees, everyone notices when
something goes wrong.
A denied claim is not just a statistic.
It is someone's afternoon.
A prior authorization is not just a workflow.
It is someone's phone call.
A payer portal is not just software.
It is someone's headache.
That makes independent practices excellent laboratories for
healthcare innovation.
If we can reduce friction there, we are solving something
real.
Five questions before you buy another billing product
Before a vendor shows you a beautiful demo, ask five
questions.
1. What problem are you solving?
If the answer is “AI-powered revenue cycle optimization,”
ask again.
What problem?
2. Where does the intervention occur?
Before documentation?
During documentation?
Before claim submission?
After denial?
3. What happens when the AI is wrong?
This question is often more revealing than the demo.
4. Can I measure the improvement?
If the vendor cannot define the baseline and outcome, be
careful.
5. Does this reduce work or redistribute work?
This is the trap.
A system can make one department faster while creating more
work somewhere else.
The total workflow is what matters.
The five metrics I would watch
Forget vanity metrics.
Start with:
Clean claim rate
How often do claims leave correctly the first time?
Denial rate
How often does the payer reject the claim?
Preventable denial rate
How many denials could the practice reasonably have
prevented?
Days in A/R
How long is money sitting unresolved?
Administrative hours per 100 claims
This last metric is underrated.
Revenue is important.
But time is also money.
And physician time is particularly expensive.
A better definition of ROI
Healthcare technology companies love saying:
“We save practices money.”
Fine.
Show me.
I want to see:
Baseline.
Intervention.
Result.
If staff previously spent 100 hours per month on a workflow
and now spend 60, show it.
If denials were 9% and become 6%, show it.
If days in A/R decline, show it.
If physicians spend less time on administrative tasks, show
it.
If nothing improves, say so.
Trust grows faster when companies admit what did not
work.
That applies to founders too.
A failure worth admitting
Healthcare entrepreneurs sometimes fall into the same trap
as healthcare institutions.
We start with the solution.
Then we look for the problem.
It is seductive.
The demo looks great.
The AI responds.
The workflow moves.
Everyone nods.
Then the real clinic gets involved.
And someone says:
“This creates three extra clicks.”
And suddenly the brilliant solution has become another
burden.
That is a useful failure.
Because the lesson is simple:
A workflow that looks elegant on a whiteboard can be
terrible at 4:47 p.m. on a Friday when the clinic is full.
The real test is not the demo.
It is Tuesday afternoon.
The humor of healthcare technology
There is an old joke in healthcare:
We have invented technology to save doctors time.
Then we spend three hours teaching doctors how to use it.
Healthcare technology sometimes resembles buying a robot
vacuum and then spending the afternoon explaining to the robot where the floor
is.
At some point, we need better design.
The best automation should not require a PhD in automation.
It should simply work.
What “good AI” should feel like
Good AI should feel less like a new employee and more like a
competent assistant.
It notices.
It remembers.
It prioritizes.
It flags.
It explains.
It stays quiet when nothing needs attention.
That last one matters.
Silence is a feature.
If the system is constantly interrupting the physician, it
is not intelligent enough.
The myth-buster
Myth 1: AI will eliminate the billing department
Probably not.
And that should not be the goal.
The better goal is to eliminate unnecessary work.
Human beings should handle judgment, exceptions,
relationships and accountability.
Machines should handle repetitive, structured tasks where
they can do so reliably.
Myth 2: More documentation prevents more denials
Not necessarily.
More documentation can also mean more burden and more
opportunities for inconsistency.
The objective is accurate and relevant documentation,
not maximal documentation.
Myth 3: Every denial is bad
No.
Some denials are appropriate.
The question is whether the denial is accurate and whether
the practice can understand why it occurred.
Myth 4: Faster claims equal better revenue
Not always.
A practice can submit claims faster and still collect
poorly.
The real question is:
How much legitimate revenue becomes collectible, and how
quickly?
Myth 5: The newest AI model wins
No.
The workflow wins.
A mediocre model embedded beautifully into a workflow can be
more valuable than an extraordinary model nobody wants to use.
Recent news: the system is starting to catch up
There is an interesting tension in healthcare right now.
Physicians are saying:
This administrative burden is hurting us.
Regulators are saying:
We need more interoperability and transparency.
Technology companies are saying:
AI can help.
And patients are saying:
I just want my care.
Those four voices need to meet.
The AMA's 2026 survey found that only 33% of physicians
believe recent insurer commitments will make a meaningful difference.
That skepticism is important.
Healthcare leaders should not respond with another promise.
They should respond with measurement.
Show physicians what changed.
Legal and ethical reality
There is no shortcut around compliance.
AI-assisted billing still requires appropriate attention to:
HIPAA
Business associate obligations
Coding compliance
Payer contracts
Documentation requirements
Fraud and abuse laws
Auditability
Data security
Human oversight
And there is a fundamental ethical principle:
Never let an algorithm's confidence become a substitute
for evidence.
If the documentation does not support something, AI should
not manufacture support.
If the system is uncertain, it should say so.
If the recommendation matters, the practice should be able
to understand how it was generated.
That is not merely good engineering.
It is good medicine.
A 30-day challenge for physician-owners
You do not need a six-month transformation project.
Try this.
Days 1–7: Find the leaks
Pull your denial data.
Find the five largest categories.
Calculate how much revenue is sitting in unresolved claims.
Days 8–14: Find the cause
For each major denial, ask:
Where did it begin?
Documentation?
Eligibility?
Authorization?
Coding?
Payer rule?
Claim formatting?
Workflow?
Days 15–21: Move upstream
Pick one recurring problem.
Try to catch it earlier.
Not ten problems.
One.
Days 22–30: Measure
Did the denial rate change?
Did staff hours change?
Did A/R change?
Did physician burden change?
If yes, expand.
If no, learn.
Then try again.
That is innovation.
Not buying software.
Learning faster.
What OnnX is trying to build
This is where my own work comes in.
I founded OnnX around a question:
What if medical billing became intelligent before the
claim was created?
Not another outsourcing company.
Not another portal.
Not another dashboard.
Not AI for the sake of putting “AI” on a website.
The goal is to explore an AI-powered medical billing
infrastructure that helps small and medium-sized practices identify
problems earlier, understand their revenue cycle better and reduce unnecessary
administrative work.
The philosophy is upstream.
Clinical information → structured intelligence →
validation → optimized claim → payer interaction → payment.
The closer we can connect those stages, the less information
should be lost between them.
That is the thesis.
It is still being built.
And it should be challenged.
Because healthcare founders should not expect physicians to
believe a vision simply because it sounds good.
The product has to earn that belief.
The bigger idea: healthcare infrastructure should
disappear
Think about electricity.
You don't walk into a room and congratulate the electrical
grid.
It works.
Think about the internet.
Most of the time, you don't think about the routing
infrastructure.
It works.
Healthcare administration should move in that direction.
The physician should not have to think about the
revenue-cycle machinery every time a patient walks through the door.
The infrastructure should quietly do its job.
When something requires human intervention, it should
explain why.
That is the future I want to see.
Invisible infrastructure.
Not invisible accountability.
Not invisible algorithms.
Invisible friction.
Why this matters for physician independence
This conversation is bigger than billing.
It is about whether physicians can operate sustainable
practices without surrendering more and more control to layers of
intermediaries.
When a physician cannot see where revenue is being lost, the
practice becomes dependent.
When the physician cannot understand why claims are denied,
the practice becomes dependent.
When every workflow requires another vendor, another portal
and another contract, complexity compounds.
Better infrastructure should give practices more agency,
not less.
That is especially important for small and medium-sized
clinics.
The patient is still the point
Let's go back to Belfast.
Dáithí Mac Gabhann came home.
He was playing football.
That is the image I want healthcare leaders to remember.
Not the transplant statistics.
Not the waiting list.
Not the legislation.
Not the technology.
The boy.
The football.
The family.
The ordinary day.
That is what successful healthcare eventually looks like.
The patient stops thinking about healthcare.
They start living.
That should be our definition of success.
The three lessons I would take from Dáithí's story
Lesson 1: Healthcare should end in life, not paperwork
The paperwork is necessary.
But it is not the outcome.
Lesson 2: Waiting is a healthcare variable
Patients wait for appointments.
They wait for authorizations.
They wait for referrals.
They wait for payments to settle.
They wait for answers.
Every unnecessary delay has a human cost.
Lesson 3: The best innovation removes friction between
people and life
That is the standard I would use.
Not:
“Does it use AI?”
Instead:
“Does it make healthcare easier to deliver and easier to
receive?”
Final Thoughts: Stop Optimizing the Wrong Thing
Healthcare has become remarkably good at optimizing
processes that should never have become this complicated.
We optimize claim submission.
We optimize denial management.
We optimize authorization workflows.
We optimize staff productivity.
We optimize documentation.
We optimize dashboards.
We optimize utilization.
We optimize everything.
Except sometimes the thing that matters most.
The patient's ability to get on with life.
Dáithí Mac Gabhann waited eight years for a heart.
Now he is home in west Belfast.
He can play football.
His family can think about tomorrow.
And somewhere in that story is a lesson for every healthcare
entrepreneur, administrator and physician-owner:
The purpose of healthcare is not to create better
paperwork.
It is to create better outcomes for human beings.
The administrative system should serve that purpose.
Not become the purpose.
Get Involved
So here is my question for physicians and clinic owners:
If you could eliminate one administrative burden from
your practice tomorrow, what would it be?
Prior authorization?
Denials?
Documentation?
Payer portals?
Eligibility?
Coding?
A/R?
Or something nobody outside your practice even knows exists?
Tell me in the comments.
Your answer may reveal a problem that thousands of other
practices are quietly experiencing.
And if this perspective resonates with you, share the
post with another physician or clinic owner who has spent too much time
fighting the system instead of caring for patients.
The future of healthcare will not be designed only by
technology companies.
It will be shaped by the people who live with the problems
every day.
Raise your hand. Join the conversation. Help define what
better healthcare infrastructure should look like.
Three Actions
Measure one source of administrative friction.
Move one recurring problem upstream.
Share what you learn with the healthcare community.
Small changes compound.
Better questions produce better systems.
And better systems give clinicians more time to do what only
humans can do.
Frequently Asked Questions
Is medical billing really connected to patient care?
Indirectly, yes.
Billing affects practice sustainability, staffing,
administrative capacity and sometimes delays in care. It should not replace
clinical quality measures, but it is part of the infrastructure supporting
care.
Should physicians become billing experts?
No.
Physician-owners should understand the major financial and
operational drivers of their practices. They should not have to become
professional coders.
Can AI eliminate denials?
No credible technology should promise zero denials.
The better objective is reducing preventable denials
and identifying patterns earlier.
Should every clinic adopt AI?
No.
Start with the problem.
If you do not know what is broken, buying AI is probably
premature.
What should I ask an AI billing vendor?
Ask:
What problem will you solve?
Where in the workflow will you solve it?
What is the baseline?
What is the measurable outcome?
What happens when the system is wrong?
Is more documentation better?
Not automatically.
The goal is accurate, relevant documentation that supports
the care provided.
What is “precision at the source”?
It means identifying and correcting predictable problems as
close as possible to where the underlying information is created rather than
waiting until the claim is denied.
What is the biggest mistake practices make?
Treating every denial as an individual problem instead of
looking for recurring patterns.
What should I measure first?
Start with clean claim rate, denial rate, preventable denial
categories, days in A/R and staff time spent on billing problems.
What does good healthcare AI look like?
It should be useful, explainable, measurable, secure and
appropriately supervised.
And ideally, it should create fewer interruptions rather
than more.
References
1. Dáithí Mac Gabhann's homecoming: Current reporting
describes Dáithí returning to west Belfast after his heart transplant and the
significance of an ordinary childhood finally becoming possible.
Read
the Irish News report on Dáithí's return home
2. Physician burden from prior authorization: The
AMA's 2026 survey provides current data on delays, abandonment of treatment,
adverse events, physician workload and burnout.
Read
the AMA 2026 prior authorization findings
3. CMS and the changing administrative infrastructure:
CMS's interoperability and prior authorization framework points toward more
electronic workflows and greater transparency around certain denial decisions.
Read
the CMS prior authorization final rule
About the Author
Dr. Daniel Cham is a physician, medical consultant
and healthcare technology entrepreneur whose work sits at the intersection of medicine,
healthcare operations, medical billing and technology.
He is the founder of OnnX, an AI-powered medical
billing SaaS initiative focused on helping small and medium-sized physician
practices reduce administrative friction, improve revenue-cycle intelligence
and gain greater visibility into the operational side of their practices.
His approach is intentionally practical:
Technology should solve a problem before it becomes a
product.
His work explores how healthcare organizations can use
intelligent automation without losing clinical judgment, human accountability
or the patient connection at the center of medicine.
Dr.
Daniel Cham on LinkedIn
Disclaimer / Note
This article is intended for general educational and
informational purposes. It does not constitute medical, legal, coding,
compliance, reimbursement, financial or professional advice. Healthcare
organizations should consider their own circumstances and seek advice from
appropriately qualified professionals before making clinical, legal, compliance
or operational decisions.
Continue the Conversation
The healthcare problems worth solving are not always the
ones receiving the most attention.
I share observations, practical strategies and lessons from
the intersection of clinical medicine, healthcare operations, technology and
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If the topic of medical billing, healthcare operations,
AI or practice innovation matters to you, take a look and use whatever is
useful for your practice.
One Last Thought
Dáithí's story gives us a beautiful but uncomfortable
benchmark.
A child waited eight years for a new heart.
Healthcare professionals fought to keep him alive.
A donor family made an extraordinary gift.
His parents, Máirtín Mac Gabhann and Seph Mac Gabhann,
spent years advocating for organ donation.
And now Dáithí is home.
Playing football.
That is the outcome.
Everything else is infrastructure.
Let's build infrastructure that gets out of the way.
Let's give physicians more time for medicine and patients
more time for life.
And let's stop confusing a perfectly processed claim with
a perfectly delivered healthcare system.
If this perspective resonates, repost it so another
physician or clinic owner can join the conversation.
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#PriorAuthorization #DenialManagement #MedicalCoding #HealthcareOperations
#ClinicalWorkflow #HealthTech #DigitalHealth #AIinHealthcare
#PatientCenteredCare #AdministrativeBurden #IndependentPhysicians #MedicalSaaS
#RevenueCycle #HealthIT #Interoperability #PracticeManagement #OnnX
