What Brooklyn Dotson, Michael Waters, and Noah Waters can teach us about medical billing, invisible outcomes, and why healthcare keeps fixing problems after they happen
“Moral agency is getting eroded.” — Kayvan
Haddadan, MD
Brooklyn Dotson received Michael Waters’s liver. Years
later, she fell in love with his brother, Noah. What can their extraordinary
story teach us about the problems we discover too late in medical billing?
In a September 27, 2026 essay, physician Kayvan Haddadan,
MD, argued that declining reimbursement, increasing patient volume,
documentation demands, payer audits, denials, and clawbacks can create a cycle
that steadily squeezes the time and autonomy physicians have to practice
medicine.
That observation got my attention.
But another healthcare story had been sitting in the back of
my mind.
It starts with a little girl named Brooklyn Dotson.
It includes a 10-year-old boy named Michael Waters.
And it eventually brings us to Noah Waters, Michael's
younger brother.
The story sounds almost too improbable to be true.
But it is.
In 2003, Brooklyn Dotson was a toddler in Kentucky with
alpha-1 antitrypsin deficiency. Her liver was failing. Her mother, Ashley
Baker, was told that without a transplant, Brooklyn might have only days to
live.
Then a liver became available.
It came from Michael Waters, a 10-year-old boy from Dayton,
Ohio, who had been struck by a car and died near his home.
Michael's family honored his wishes.
Two years before his death, Michael had learned about organ
donation and told his parents that if something happened to him, he wanted his
organs donated.
His mother, Tina Poteet, later said:
“It was Michael’s gift.”
Michael's liver saved Brooklyn's life.
But that was only the beginning.
A few months later, the two families connected.
Brooklyn's mother, Ashley Baker, and Michael's mother, Tina
Poteet, exchanged letters.
The families eventually met.
Brooklyn and Noah Waters met when Noah was about six.
They became friends.
The families stayed connected for years.
Brooklyn grew up.
Noah grew up.
They lived separate lives.
They dated other people.
They went to college.
They started careers.
Then something neither family could have predicted happened.
The friendship became a relationship.
Noah proposed to Brooklyn in December 2025.
The little girl who received Michael Waters' liver was now
engaged to Michael's younger brother.
The story is extraordinary because nobody could have
predicted the outcome when the transplant took place.
Nobody knew where the chain of events would eventually lead.
A donor.
A grieving family.
A liver.
A transplant.
A child surviving.
Two families meeting.
Two children becoming friends.
Two adults falling in love.
One healthcare event.
Decades of consequences.
The Washington Post reported that Brooklyn and Noah were
scheduled to marry on September 26, 2026.
Think about that.
The most important outcome of that transplant wasn't
necessarily the outcome anyone could have measured at the time.
It was everything that happened afterward.
And that is where this beautiful human story connects to
something much less beautiful:
medical billing.
Because healthcare has a habit of measuring the last thing
that happened.
The payment.
The denial.
The readmission.
The authorization.
The audit.
The appeal.
The clawback.
The missed appointment.
The incomplete note.
But the event we see at the end often started much earlier.
And sometimes, by the time we see it, we are already too
late to prevent it.
That is the problem I think healthcare needs to talk about.
Not just better billing.
Not just faster billing.
Not just more automation.
Better upstream information.
The Question I Would Ask Every Physician Owner
Here is my deliberately uncomfortable question:
What if your denial is not the problem?
What if it is merely the first place the problem becomes
visible?
That sounds like semantics.
It isn't.
Imagine a claim gets denied because the documentation does
not support the billed service.
The billing department sees the denial.
So the billing department works the denial.
Reasonable.
But now go backward.
Why was the documentation incomplete?
Maybe the physician was seeing 30 patients.
Why 30?
Because reimbursement is under pressure.
Why does that matter?
Because higher volume can compress the time available for
documentation.
Why does documentation matter?
Because the payer evaluates the record retrospectively.
Why does that matter?
Because a clinical decision made under time pressure may
later be judged through a very different administrative lens.
Now we have a much bigger story.
The denial wasn't born in the billing office.
It may have started with time.
And the time problem may have started with economics.
And the economics may have started with reimbursement
pressure.
And suddenly the denial has become the final visible symptom
of a much larger system.
That is why simply asking, "How do we appeal
this?" can be intellectually lazy.
Sometimes the better question is:
"Where did this problem actually begin?"
Healthcare Loves Firefighters
Healthcare has a strange relationship with heroism.
We love the person who stays late.
The nurse who catches the problem.
The physician who calls the insurer.
The biller who rescues the account.
The practice manager who fixes the mess.
The employee who knows the payer rule nobody else knows.
We call these people indispensable.
And they often are.
But there is a hidden danger.
A great firefighter can make a terrible fire-prevention
program look successful.
If your best biller rescues 300 claims every month, that is
not necessarily evidence that your revenue cycle is healthy.
It may be evidence that your biller is extraordinary.
Those are very different things.
The Billing Department May Be the Emergency Department of
Your Revenue Cycle
Think about that analogy.
The emergency department doesn't create every illness.
It receives the consequences.
Something happened earlier.
A disease developed.
An injury occurred.
A symptom was ignored.
An infection progressed.
Then the patient arrives at the emergency department.
The emergency team responds.
Billing can work the same way.
The billing team receives:
- Incorrect
demographic information
- Coverage
problems
- Missing
authorization
- Documentation
gaps
- Coding
inconsistencies
- Payer
edits
- Claim
rejections
- Denials
- Underpayments
Then billing responds.
But what if we stopped thinking of billing as the place
where problems are solved?
What if we treated it as the place where system failures
are diagnosed?
That would change everything.
The Revenue Cycle Is Not a Billing Department
This may be the most important sentence in the article:
The revenue cycle begins before the claim exists.
It begins when someone schedules the patient.
It continues when eligibility is checked.
It continues when the referral is processed.
It continues when prior authorization is evaluated.
It continues when the patient is registered.
It continues when the physician documents the encounter.
It continues when the diagnosis is captured.
It continues when the procedure is coded.
Only then does the claim enter the billing process.
So why do we act as though billing owns the entire revenue
cycle?
Because the biller is the person holding the bag when
something goes wrong.
That's not ownership.
That's inheritance.
The Information Chain
Consider a typical patient journey:
Patient → Scheduling → Registration → Eligibility →
Referral → Authorization → Clinical Encounter → Documentation → Coding → Claim
→ Payer → Payment
Now imagine every arrow represents a handoff.
Every handoff introduces potential variation.
A name can be entered differently.
An insurance number can be wrong.
A referral can be missing.
An authorization can expire.
A diagnosis can be documented differently.
A clinical note can lack a required element.
A payer rule can change.
A code can be selected incorrectly.
Then the claim gets denied.
And someone asks:
"Why did the payer do that?"
Maybe the payer did exactly what the information they
received told them to do.
That does not necessarily mean the payer is right.
It means the interesting question is earlier:
What information reached the payer, and how did it get
there?
The Hidden Cost Nobody Puts on the P&L
Physician owners usually see billing costs.
They see the billing company's invoice.
They see payroll.
They see software subscriptions.
They see clearinghouse fees.
They see collection percentages.
But there is another cost.
It is hiding inside everyone's job.
The nurse spends 20 minutes dealing with an authorization.
The front desk spends 15 minutes correcting eligibility.
The physician spends 10 minutes answering a documentation
question.
The biller spends 30 minutes fixing a claim.
The practice manager spends an hour investigating why
payment is delayed.
Individually, none looks catastrophic.
Together?
That is a staffing model.
You may not have hired another full-time employee.
But your practice may be behaving as though you did.
I call this the 1.2 FTE problem.
The practice has an invisible employee whose job is:
Fixing things that should not have broken.
That employee never gets a badge.
Never gets a performance review.
Never appears on the organizational chart.
But you are paying for them.
The Numbers Behind the Friction
The administrative burden is not theoretical.
CMS says prior authorization requests can cost providers
approximately $20–$50 per hour in staff time and take an average of 13
hours per week, which CMS estimates can amount to approximately 700
hours of administrative time per provider annually. CMS is moving toward
electronic prior authorization beginning in 2027 for certain processes.
The AMA has also reported substantial physician concern
about prior authorization burden and its relationship to delays and burnout.
Those numbers matter.
But I think they tell only half the story.
Because the real question isn't simply:
How many hours are we spending?
It is:
Why are we spending them?
That is a different question.
And a much more useful one.
Recent News: Healthcare Is Still Trying to Solve the
Downstream Problem
This week's healthcare news provides several reminders that
the pressure on physician practices is not disappearing.
The Medical Group Management Association is currently
highlighting changes affecting physician payment, MIPS, prior authorization,
Medicaid, and other federal issues facing practices in 2027.
Medical Economics is also reporting on physician
productivity and the financial pressure created when operating costs rise
faster than revenue.
And this week's healthcare technology conversation is
increasingly shifting toward a more practical question about AI: not simply
whether AI can produce impressive outputs, but whether it can improve care
quality and return meaningful time to clinicians.
That is an important shift.
Because "AI can do this" is not a healthcare
strategy.
The better question is:
"What unnecessary work disappears because AI
exists?"
If the answer is "none," we have created another
tool.
Not necessarily another solution.
Three Experts, Three Lessons
Atul Gawande: Complexity Is the Enemy Hiding in Plain
Sight
Surgeon and author Atul Gawande has spent years examining
complexity, checklists, systems, and the challenge of delivering reliable care
when professionals operate inside complicated environments.
The billing lesson is straightforward:
Good people cannot reliably compensate for infinite
complexity.
Eventually, complexity wins.
Not because your staff is incompetent.
Because humans have limits.
We forget.
We misunderstand.
We get interrupted.
We make assumptions.
We work under pressure.
The answer is not to demand superhuman performance.
The answer is to design systems that require less heroism.
Peter Pronovost: Standardization Can Protect Human
Judgment
Patient-safety researcher Peter Pronovost became widely
known for demonstrating how standardized processes can reduce preventable harm.
The lesson is not "make medicine robotic."
Quite the opposite.
The point is to standardize the predictable parts so
professionals have more capacity for the unpredictable parts.
That applies to revenue cycle management.
Do not make physicians memorize payer rules.
Do not make nurses memorize authorization requirements.
Do not make billers compensate forever for missing
information.
Standardize the predictable.
Protect human attention for the exceptional.
Don Berwick: Fix the System
Don Berwick's work in healthcare quality has repeatedly
emphasized system improvement rather than simply demanding more from
individuals.
This may be the most important lesson for independent
practices.
When the same problem happens repeatedly, ask:
Is this really an employee problem?
Or is it a system problem?
If five different employees make the same mistake, maybe you
don't have five bad employees.
Maybe you have one bad process.
That distinction can save a lot of money.
And a lot of blame.
A More Provocative Definition of "Efficiency"
Healthcare often defines efficiency as:
Doing more with less.
I think that definition is incomplete.
A better definition is:
Doing less unnecessary work.
Those are not the same thing.
Doing 40 patient visits instead of 30 may look efficient.
Unless it produces more documentation problems.
Unless staff spend more time on follow-up.
Unless denials increase.
Unless physician burnout increases.
Unless patients wait longer.
Unless the practice loses people.
You haven't necessarily improved the system.
You may simply have squeezed it harder.
The Productivity Trap
This week's healthcare discussion around physician
productivity makes the tension especially visible.
When operating costs rise faster than revenue, the obvious
response is:
See more patients.
More visits.
More volume.
More productivity.
It makes financial sense.
At least initially.
But every system has a carrying capacity.
At some point, more volume creates more:
- Documentation
- Orders
- Messages
- Authorizations
- Claims
- Corrections
- Follow-up
- Administrative
work
And now the system is producing more work faster than people
can absorb it.
The physician becomes more "productive."
The practice becomes less manageable.
That's an interesting definition of productivity.
What If Productivity Is the Wrong Metric?
Here's my contrarian question:
What if the most productive physician is not the
physician who sees the most patients?
What if it is the physician whose practice creates the least
unnecessary work per patient?
That would change the dashboard.
Instead of simply counting encounters, we could also look
at:
Administrative friction per encounter.
How many touches?
How many corrections?
How many messages?
How many authorization loops?
How many documentation queries?
How many claim interventions?
How many minutes of physician time occur after the patient
has already left?
Now we are measuring something physicians actually
experience.
The Problem With "Best Practices"
Healthcare loves the phrase best practice.
Sometimes I think we use it because it sounds better than:
"This is how we have always done it."
A large health system may have a 25-person revenue-cycle
department.
An independent practice may have one biller and a practice
manager who also handles HR.
Those two organizations should not necessarily use the same
workflow.
The question is not:
"What is the industry's best practice?"
Ask:
"What is the simplest reliable practice for our
environment?"
Simple wins surprisingly often.
Myth Buster
Myth: More billing staff will solve the problem.
Maybe.
But if the workflow generates unnecessary work, you may
simply be hiring people to process the consequences.
More capacity is not the same as less waste.
Myth: A low denial rate means the system is healthy.
Not necessarily.
You can have a low denial rate and still have:
- Underpayments
- Delayed
claims
- Excessive
manual work
- Authorization
burden
- Documentation
queries
- Missed
charges
- Poor
cash flow
A metric is useful only in context.
Myth: A denial is a billing problem.
Sometimes.
But many denials are downstream manifestations of upstream
information or workflow problems.
The right answer is to identify the actual cause rather than
assume the department where the problem appeared caused it.
Myth: AI solves bad data.
No.
AI can process bad data very efficiently.
That is not the same as fixing it.
Bad data plus powerful automation can create very
sophisticated mistakes.
Myth: Automation means removing humans.
Not necessarily.
Good automation should remove unnecessary human work.
It should not automatically remove human accountability.
What AI Should Actually Do
I am a physician.
I am also building a healthcare technology company.
So I have an obvious interest in AI.
But I am increasingly skeptical of the phrase:
"AI-powered."
Those two words can mean almost anything.
The better question is:
What exactly is the machine doing?
Is it extracting information?
Classifying information?
Detecting an exception?
Predicting a risk?
Applying deterministic rules?
Recommending an action?
Submitting something automatically?
Those are very different things.
For healthcare billing, I believe a useful architecture
looks more like:
AI extracts.
Rules determine.
Humans oversee exceptions.
That is much easier to trust than:
AI decides everything.
The OnnX Thesis
This is the problem I am building around with OnnX.
My thesis is simple:
Healthcare billing is often a data-quality problem before
it becomes a billing problem.
Most revenue-cycle activity happens after information has
already moved through multiple hands and systems.
By then, the practice is reacting.
OnnX is designed around moving intelligence upstream.
That means helping practices identify problems earlier in
the patient-to-claim journey.
The objective is not to create another dashboard.
It is not to add another inbox.
It is not to give physicians another piece of software to
learn.
And it is certainly not to make physicians into part-time
billers.
The objective is to reduce the correction loop.
Less rework.
Less ambiguity.
Less manual chasing.
Less dependence on tribal knowledge.
More predictable revenue-cycle operations.
The Correction Loop
Here is the loop I want to break:
Information enters.
Something is incomplete.
Nobody notices.
The encounter occurs.
The claim is generated.
The payer rejects it.
Someone discovers the problem.
Someone investigates.
Someone contacts someone else.
Someone fixes the data.
Someone resubmits the claim.
Someone follows up.
Someone waits.
Someone checks again.
Eventually, money arrives.
Everyone celebrates.
And then the same thing happens next Tuesday.
That is not a workflow.
That's a subscription to frustration.
The Upstream Alternative
Now imagine a different model.
The system sees the information earlier.
A potential problem is identified.
The appropriate person gets an actionable exception.
The issue is corrected before the claim exists.
The claim goes out with fewer avoidable problems.
The billing team focuses on actual exceptions rather than
routine cleanup.
The physician is interrupted less often.
The patient experiences less administrative friction.
The practice gets paid with fewer correction loops.
That is the direction I believe healthcare technology should
pursue.
The Five Questions Every Clinic Owner Should Ask
1. Where does information first enter our system?
Usually the front end.
That makes registration more important than many practices
realize.
2. Where does information change?
Every transformation is a risk point.
3. Where does someone have to remember a rule?
That is a candidate for standardization or automation.
4. Where do humans repeatedly correct the same problem?
That is a root-cause signal.
5. How much physician time is consumed downstream?
This one gets overlooked.
The physician's time is not just a clinical resource.
It is the most expensive attention in many practices.
A Five-Day Upstream Audit
You do not need a consulting firm.
You do not need a six-month project.
Try this.
Day 1: Pull 10 recent denials
Don't choose only the biggest ones.
Choose a representative sample.
Day 2: Trace each denial backward
Ask:
Where did the problem begin?
Day 3: Group the causes
Use five categories:
Data
Workflow
Authorization
Documentation
Payer rule
Day 4: Find repetition
Which problem occurred more than once?
That's your signal.
Day 5: Change one upstream step
Not ten.
One.
Then measure the result.
Metrics Worth Watching
Traditional revenue-cycle metrics still matter.
Track:
Days in accounts receivable
Clean-claim rate
Denial rate
Net collection rate
Payment variance
Aging
But add:
Preventable denial rate
Manual touches per claim
Administrative touches per encounter
Staff minutes spent on rework
Documentation query rate
Authorization exception rate
Repeat-denial rate
Time from encounter to claim-ready status
These metrics tell a different story.
They tell you how hard the system is making people work.
The Metric I Want More Practices to Measure
If I could add one number to every physician practice
dashboard, it would be:
Administrative Touches Per Encounter
How many people have to touch the information before the
practice gets paid?
One?
Three?
Seven?
Ten?
Fifteen?
The number is revealing.
Because every touch creates an opportunity for:
- Delay
- Error
- Duplication
- Miscommunication
- Cost
Reducing touches does not mean removing necessary human
judgment.
It means removing unnecessary handoffs.
Legal and Compliance Considerations
Upstream automation does not remove compliance
responsibilities.
It can actually make governance more important.
Practices should consider:
HIPAA privacy and security
Business associate requirements
Access controls
Audit trails
Documentation integrity
Coding compliance
Payer contracts
Medicare and Medicaid requirements
State requirements
Human oversight
Data retention
Security incident response
There is also a basic principle that should never disappear:
The practice remains accountable for what it submits.
Software does not become the legal owner of the claim simply
because software touched it.
"The algorithm did it" is not a compliance
strategy.
Ethical Considerations
Revenue-cycle technology deals with information about real
people.
A claim represents a patient.
A diagnosis represents a patient.
A denial may delay care.
A documentation query consumes physician attention.
A prior authorization request can delay treatment.
That means optimization has an ethical dimension.
The goal should not be:
Maximum automation.
It should be:
Minimum unnecessary friction while preserving accuracy,
accountability, privacy, and clinical judgment.
That distinction matters.
Tools and Resources
A practical revenue-cycle improvement toolkit can be
surprisingly simple.
Root-Cause Log
Track:
- Payer
- Service
- Denial
- Cause
- Origin
- Preventability
- Staff
time
- Resolution
Payer Rule Tracker
Record important changes in:
- Authorization
- Documentation
- Coverage
- Coding
- Filing
deadlines
Exception Queue
Surface only cases that need attention.
Process Map
Map the patient journey from scheduling through payment.
Monthly Friction Review
Ask:
Where did people spend time fixing something that should
have been correct the first time?
That question alone can reveal a lot.
The Biggest Mistake Founders Make
Healthcare founders often start with the technology.
They ask:
What can AI do?
I think the better question is:
Where is the human being wasting time?
Then:
Why does that work exist?
Then:
Can the underlying problem be prevented?
Only then:
Can technology help?
That sequence matters.
Otherwise, we risk building very sophisticated solutions to
very poorly understood problems.
The Biggest Mistake Physicians Make
Physicians often assume administrative complexity is simply
part of practicing medicine.
It isn't necessarily.
Some complexity is unavoidable.
Some is useful.
Some is necessary for safety.
But some is simply accumulated history.
One payer added a rule.
Another added an exception.
The EHR created a field.
Someone created a spreadsheet.
A staff member developed a workaround.
The workaround became policy.
Five years later, everyone follows it.
Nobody remembers why.
Welcome to healthcare.
The Bigger Opportunity
The next generation of healthcare technology may not come
from making every process more sophisticated.
It may come from making them simpler.
Fewer handoffs.
Cleaner information.
Earlier detection.
Better exception management.
Less tribal knowledge.
More reliable workflows.
That is not as flashy as an AI agent doing everything.
But it may be more useful.
What the Brooklyn Dotson Story Really Teaches Us
Let's go back to Brooklyn.
Michael Waters' family could not know what would happen
after they honored his wish.
They knew only that they wanted to respect what Michael had
told them.
His liver went to Brooklyn Dotson.
Brooklyn survived.
The families connected.
Noah and Brooklyn grew up.
Life continued.
And more than twenty years later, the original act of
generosity had become part of a completely different story.
That is healthcare.
Not a transaction.
A chain of consequences.
Some measurable.
Some invisible.
Some immediate.
Some decades away.
The mistake we make is assuming the outcome is the last
event.
It isn't.
The outcome is everything that happens afterward.
And That Is Also True of Medical Billing
A denial is not the end of a claim.
It creates work.
That work creates cost.
That cost consumes staff time.
That time can affect workload.
Workload can affect physician experience.
Physician experience can affect retention.
Retention can affect continuity.
Continuity can affect the patient experience.
The chain continues.
So perhaps the right way to think about revenue cycle is
not:
"How much money did we collect?"
But:
"How much unnecessary friction did it take to
collect it?"
That is a very different metric.
Three Things I Would Change Tomorrow
First: Stop treating every denial as an isolated event.
Look for patterns.
Second: Stop measuring only recovery.
Measure prevention.
Third: Stop asking employees to compensate for broken
workflows.
Fix the workflow.
That sounds simple.
It isn't always easy.
But it is where the leverage is.
A Challenge for Physicians and Clinic Owners
Take your last ten denials.
Don't open the payer portal yet.
Don't call the billing company.
Don't blame the payer.
Don't blame the biller.
Instead ask:
Where did this problem first become possible?
You might be surprised by the answer.
It may not be in billing.
It may be in scheduling.
Registration.
Eligibility.
Documentation.
Authorization.
Communication.
Or simply a rule nobody knew had changed.
That is the point.
The visible problem is often not the original problem.
The Future of Revenue-Cycle Management
I don't think the future is humans versus AI.
That's an unnecessarily dramatic framing.
The future is more likely:
Humans doing the work that requires judgment.
Machines handling predictable information processing.
Rules enforcing deterministic requirements.
Systems identifying exceptions earlier.
People intervening when context matters.
And perhaps most importantly:
Data becoming useful before it becomes a claim.
That is where healthcare technology becomes interesting.
Final Thoughts: Look Upstream
The story of Brooklyn Dotson, Michael Waters, and Noah
Waters is extraordinary because it reminds us that the consequences of a
healthcare decision can extend far beyond the moment in which the decision was
made.
A 10-year-old boy's decision about organ donation became a
liver transplant.
The transplant became survival.
Survival became a childhood.
The childhood became a life.
That life eventually became a relationship.
And that relationship became a marriage.
No dashboard captured the entire chain.
No metric predicted it.
No one could see the final outcome at the beginning.
Healthcare is full of chains like this.
We simply don't always notice them.
The same is true in the revenue cycle.
The denial we see today may have started weeks earlier.
The payment delay may have started at registration.
The documentation problem may have started with time
pressure.
The authorization problem may have started with a rule
nobody knew had changed.
The revenue leakage may have started with information that
was never structured correctly.
So perhaps the most important question for physician
practices is not:
"How do we get better at fixing denials?"
It is:
"How do we get better at preventing the conditions
that create them?"
That is the difference between reacting to the revenue cycle
and designing it.
And that is where I believe healthcare technology has an
opportunity to become genuinely useful.
Not by adding another layer.
Not by replacing physicians.
Not by creating another dashboard nobody opens.
But by making the information that matters available before
the problem becomes expensive.
Look upstream.
Fix the information before you fix the claim.
And measure how much unnecessary work your system creates
before asking your people to work harder.
Get Involved: Continue the Conversation
Here is the question I want to leave with physicians,
practice owners, administrators, and medical billers:
What is the most expensive problem in your revenue cycle
that everyone has simply learned to live with?
Is it prior authorization?
Eligibility?
Documentation?
Coding?
Payer changes?
Denials?
Underpayments?
Or something nobody has named yet?
Leave a comment and tell me where the friction begins in
your practice.
If you've seen the same problem repeatedly, share your
experience. Someone reading this may be dealing with exactly the same
issue.
And if this perspective resonates, repost the article
so another physician or clinic owner can rethink the assumption that every
billing problem belongs to the billing department.
The conversation should move upstream.
The opportunity is there.
Let's start looking for it.
About the Author
Dr. Daniel Cham is a physician, healthcare
strategist, and founder of OnnX, an AI-powered medical billing SaaS
focused on helping small and medium-sized physician practices reduce
administrative friction and address revenue-cycle problems upstream.
His work explores the intersection of clinical workflows,
healthcare operations, medical billing, data quality, and practical healthcare
technology.
His perspective is straightforward:
Healthcare technology should reduce friction rather than
create another layer of it.
Connect
with Dr. Daniel Cham on LinkedIn
Disclaimer / Note
This article is provided for general educational and
informational purposes. It discusses healthcare operations, medical billing,
technology, and administrative processes at a general level and does not
constitute medical, legal, coding, compliance, reimbursement, or financial
advice.
Specific requirements can vary according to specialty,
payer, contract, jurisdiction, patient circumstances, and organizational
policy. Readers should obtain appropriate professional guidance before making
decisions concerning their individual practices or organizations.
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Three References
1. Kayvan Haddadan, MD — “Moral agency in medicine is
being squeezed by payer audits.”
Published September 27, 2026, this physician perspective examines the
relationship among reimbursement pressure, physician volume, documentation,
payer audits, denials, and professional autonomy.
Read
the physician commentary
2. Healthcare IT News — “What it will take for AI to
deliver better clinical decisions.”
Published September 28, 2026, this analysis focuses on the practical value of
healthcare AI, including care quality and time returned to clinicians rather
than technology for its own sake.
Read
the Healthcare IT News article
3. CMS — Electronic Prior Authorization.
CMS outlines the administrative burden associated with prior authorization and
its work toward electronic prior authorization, including implementation steps
for providers and health IT vendors.
Read
the CMS guidance
The Human Story Behind the Argument
For readers who want to understand where this article began,
the story of Brooklyn Dotson, Michael Waters, Noah Waters, Ashley Baker, and
Tina Poteet was reported by The Washington Post.
Brooklyn received Michael's liver after he died at age 10.
His mother, Tina Poteet, had learned that Michael wanted to be an organ donor.
Poteet later connected with Brooklyn's mother, Ashley Baker, and the two
families remained close. Brooklyn and Noah Waters eventually developed a
romantic relationship and became engaged.
It is worth reading the original story because the details
matter.
The healthcare lesson is not that organ donation is somehow
equivalent to medical billing.
It isn't.
The connection is more fundamental:
We rarely know the full consequence of a healthcare
decision when we make it.
That is precisely why upstream decisions deserve more
attention.
Hashtags
#MedicalBilling #RevenueCycleManagement
#HealthcareOperations #PhysicianPractice #IndependentPractice
#MedicalPracticeManagement #HealthcareTechnology #HealthcareAI
#PriorAuthorization #DenialManagement #PhysicianEntrepreneur #HealthTech
#ClinicalWorkflow #HealthcareInnovation #DataQuality #OnnX
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help other physicians and clinic owners rethink how billing problems begin.
