The future of healthcare may depend less on what AI can replace—and more on what it can give back: time, attention, and human connection.
“We're talking about the delivery of health care to
people.” — John Whyte, MD, MPH, CEO, American
Medical Association, August 19, 2026
A Story About a 2-Year-Old That Has Almost Nothing to Do
With AI
On August 5, 2026, 2-year-old Malachi LeBlanc was
found unresponsive at a community pool in San Antonio.
His parents, Grace LeBlanc and Myron LeBlanc, rushed
to the hospital.
They waited.
They hoped.
They prayed.
They wanted the kind of miracle every parent wants when
their child is fighting for his life.
But the miracle did not come.
Malachi was declared brain-dead.
Then came an impossible conversation.
The Texas Organ Donation Alliance approached Grace
and Myron about organ donation.
Grace's first reaction was understandable.
This was her baby.
She did not want to think about donation.
She wanted her son back.
But then she thought about another family.
Another parent sitting beside another hospital bed.
Another child waiting for a transplant.
Another person praying for the miracle that Grace and Myron
would not receive.
The LeBlanc family said yes.
Grace later explained:
“If we weren’t going to receive the miracle we wanted, I
wanted Malachi to be that miracle for someone else.”
That sentence stopped me.
Not because it is dramatic.
Because it exposes something we sometimes forget about
healthcare.
Medicine is not ultimately about procedures, claims,
codes, software or machines.
It is about people.
And that leads to a question I think every physician, clinic
owner and healthcare technology founder should be asking:
If technology is supposed to make healthcare better, why
are so many clinicians still spending so much of their time doing work that has
nothing to do with caring for patients?
That is the question behind this article.
And it is not really an AI question.
It is a human-time question.
The AI Debate May Be Asking the Wrong Question
Healthcare is having a very loud conversation about AI.
Will AI replace physicians?
Will AI diagnose disease?
Will AI write notes?
Will AI code?
Will AI replace billing staff?
Will AI take jobs?
Will AI hallucinate?
Will AI make healthcare safer?
These are legitimate questions.
But I think we are missing a more important one.
What should humans actually be doing?
Because there is another possibility we rarely discuss.
Maybe the greatest threat to human-centered healthcare isn't
that AI will take too much work away from physicians.
Maybe it is that administrative work will continue taking
too much human time away from physicians.
Read that again.
The debate is usually:
AI versus humans.
Perhaps the better debate is:
Which work belongs to humans, and which work should
machines help with?
That changes everything.
The Claim Is Not the Patient
A claim has a diagnosis.
A procedure.
A payer.
A charge.
A code.
A status.
A dollar amount.
A denial reason.
But the claim itself is not the patient.
A denial is not the patient.
A prior authorization is not the patient.
An A/R balance is not the patient.
A payer portal is not the patient.
These are representations of healthcare.
They are not healthcare itself.
And yet physicians increasingly spend enormous amounts of
time navigating them.
That creates an uncomfortable contradiction.
We say:
"Patients come first."
Then we build workflows that consume the very resource
clinicians need to put patients first:
time.
The Hidden Enemy Nobody Wants to Talk About
Healthcare loves visible villains.
High drug prices.
Insurance companies.
Government regulation.
AI.
Hospital consolidation.
Administrative complexity.
But there is a quieter problem.
Work that nobody has stopped to question.
A staff member checks something.
Someone copies it.
Someone re-enters it.
Someone reviews it.
Someone approves it.
Someone submits it.
Someone follows up.
Someone documents the follow-up.
Someone follows up again.
And because everyone has become accustomed to the workflow,
the organization starts calling it:
"the process."
But "the process" is not a defense.
It is a question.
Why does the process exist?
Who benefits from it?
What happens if we remove it?
Can the step be simplified?
Can software perform it?
Can AI assist?
Does a human really need to touch it?
Those questions are far more important than:
"Where can we add AI?"
A Contrarian Idea: Stop Automating First
This may sound strange coming from an AI founder.
But here is my advice:
Do not automate your workflow first.
Question it first.
Then eliminate.
Then simplify.
Then automate.
That sequence matters.
Because if you automate unnecessary work, you haven't
innovated.
You have simply created a faster way to do unnecessary work.
That is not transformation.
That is high-speed bureaucracy.
The Four-Letter Word That Healthcare Avoids
There is a word healthcare organizations sometimes struggle
to say:
STOP.
Stop doing this.
Stop entering that.
Stop checking the same thing three times.
Stop maintaining that spreadsheet.
Stop sending that fax.
Stop requiring the physician to review something that
doesn't require physician judgment.
Stop asking staff to perform work because "that's how
we've always done it."
We are remarkably good at adding.
Add a portal.
Add a form.
Add a field.
Add a rule.
Add a workflow.
Add a vendor.
Add an approval.
We are much worse at subtracting.
But subtraction may be the most important healthcare
innovation of all.
What Malachi's Story Reminds Us About Time
Think about Grace and Myron LeBlanc.
When your child is critically ill, you do not care about
productivity.
You do not care about dashboards.
You do not care about KPIs.
You care about one thing:
your child.
Every minute becomes meaningful.
Every conversation matters.
Every decision matters.
That is an extreme example.
But healthcare is filled with people for whom time matters.
A patient with cancer.
A frightened parent.
An elderly patient living alone.
Someone receiving a new diagnosis.
Someone waiting for surgery.
Someone trying to understand a confusing bill.
Someone whose symptoms have been ignored for months.
The healthcare system does not have an unlimited supply of
human attention.
Attention is a scarce clinical resource.
We should treat it that way.
Physician Time Is a Clinical Resource
We routinely measure:
Blood pressure.
Heart rate.
Length of stay.
Readmission.
Mortality.
Complication rates.
Patient satisfaction.
Revenue.
A/R days.
Denial rates.
But what about:
Human attention?
How many minutes does a physician spend on administrative
work that does not require a physician?
How many hours does a nurse spend searching for information?
How much staff time is consumed by rework?
How much cognitive energy disappears into payer portals?
How much physician attention is left at 6 p.m.?
Those are not merely workforce questions.
They are care-quality questions.
Because tired, distracted, overloaded humans are still
humans.
The Best AI May Be the AI You Barely Notice
This is another contrarian idea.
Healthcare AI doesn't have to be spectacular.
It doesn't need to impress a conference audience.
It doesn't need a flashy demo.
It doesn't need to announce itself every five minutes.
The best AI may quietly do something incredibly boring.
It notices a claim is likely to fail.
It retrieves the relevant information.
It identifies the likely problem.
It explains why.
It recommends an action.
A human reviews it.
The workflow continues.
Nobody applauds.
The payment arrives.
And the physician gets home on time.
That is a successful AI story.
The Workflow Nobody Sees
Consider the medical billing journey:
Patient
↓
Documentation
↓
Coding
↓
Claim
↓
Payer
↓
Adjudication
↓
Payment or Denial
↓
Appeal
↓
Payment
↓
A/R
On paper, it looks simple.
In practice, it can become a maze.
One missing detail can create another task.
One unclear code can create rework.
One payer-specific requirement can create a denial.
One denial can trigger an appeal.
One appeal can create more documentation.
One delayed payment can affect cash flow.
And suddenly a five-minute administrative problem becomes a
two-week operational problem.
That is why billing should be viewed as a workflow,
not merely a department.
The Most Important Billing Question Isn't "How Do We
Collect?"
It is:
"Why did we have to work this claim so many
times?"
That is a different question.
And it moves the organization upstream.
Instead of constantly treating symptoms, the practice starts
searching for patterns.
Why are these claims denied?
Why does this payer behave differently?
Why are these codes repeatedly corrected?
Why does this documentation problem keep appearing?
Why does staff have to manually search for this information?
Why does the physician keep getting pulled into this?
That is where intelligence becomes useful.
From Reactive Billing to Predictive Billing
Imagine two revenue-cycle systems.
System A tells you:
"Your claim was denied."
System B tells you:
"This claim has characteristics associated with a
high probability of denial. Here is the likely reason. Here is the evidence.
Here is what can be corrected before submission."
The second system changes the workflow.
It moves the organization from:
React → repair
to:
Predict → prevent
That is a much more interesting application of AI.
And it doesn't require replacing the people who understand
the practice.
It requires giving those people better information at the
right moment.
This Is Where Human-in-the-Loop Matters
There is a dangerous idea spreading through technology:
Full autonomy equals progress.
Not necessarily.
In healthcare, sometimes the smartest system is the one that
says:
"I am not sure. Please review this."
That is not failure.
That is responsible system design.
A billing AI should be able to:
Recommend.
Explain.
Prioritize.
Retrieve.
Summarize.
But consequential decisions may still require a person.
The architecture should often look like:
AI identifies
→
AI explains
→
Human reviews
→
Human approves
→
System executes
→
System records
That is not less intelligent.
It is more mature.
Three Experts, Three Lessons
Francis W. Peabody: Don't Lose the Patient
Peabody's famous statement about caring for patients sounds
almost quaint in the age of AI.
It isn't.
It may be more important now than ever.
Technology should strengthen the clinician-patient
relationship.
If it creates distance, distraction and additional work, we
should question it.
The purpose of healthcare technology is not to make
healthcare more technological.
It is to make healthcare better.
Atul Gawande: Complexity Changes the Game
Gawande has written extensively about the increasing
complexity of modern medicine.
The lesson is simple:
Human beings cannot reliably manage unlimited complexity
through memory and effort alone.
We need systems.
We need checklists.
We need better processes.
We need tools that reduce cognitive load.
Revenue-cycle management is no different.
If a payer requirement is predictable, why should every
staff member rediscover it?
If a denial pattern repeats, why should the practice treat
every denial as brand new?
If information exists somewhere in the system, why should
someone spend 20 minutes searching for it?
Good systems turn repeated knowledge into reusable
knowledge.
Don Berwick: Fix the System
Berwick's work in healthcare quality repeatedly points
toward a powerful principle:
Don't blame the person before examining the system.
When a staff member makes the same mistake repeatedly, ask
why.
When physicians struggle with administrative workflows, ask
why.
When claims repeatedly fail, ask why.
The answer may not be:
"People need more training."
It may be:
"The workflow is poorly designed."
That distinction can save enormous amounts of time.
The Small Practice Problem
Large organizations have departments.
Small practices have people.
Often the same person is:
Physician
Owner
Employer
Manager
Recruiter
Clinical leader
Business operator
And sometimes:
Billing problem solver.
That is too much.
Not because physicians cannot learn billing.
They can.
But because there is a finite amount of attention available
each day.
Every hour spent on low-value administrative work is an hour
that cannot be spent elsewhere.
The $64,000 Question
Suppose a physician could eliminate five hours of repetitive
administrative work every week.
What is that time worth?
The obvious answer is financial.
But consider the less obvious value.
Five hours could mean:
More patient visits.
More follow-up.
More time with complex cases.
More teaching.
More practice development.
More family time.
More rest.
More thinking.
More medicine.
The return on automation isn't always:
more revenue.
Sometimes it is:
more life.
The Medical Billing Workflow Needs a Redesign
Here is the framework I would use.
1. Map
Document the complete journey.
Don't guess.
Follow the work.
2. Measure
Record time, cost and error rates.
3. Eliminate
Remove unnecessary steps.
4. Simplify
Reduce handoffs.
5. Standardize
Create repeatable processes.
6. Automate
Use technology for predictable work.
7. Escalate
Send exceptions to humans.
8. Audit
Track what happened.
9. Learn
Use outcomes to improve the workflow.
10. Repeat
Optimization is not a one-time project.
Where OnnX Fits
This is the thinking behind OnnX.
The goal isn't to create another piece of software that
physicians have to learn.
The goal is to rethink how repetitive medical-billing work
gets done.
Imagine a workflow where:
A claim is submitted.
The system monitors it.
A problem appears.
AI interprets the problem.
Relevant information is retrieved.
The probable cause is identified.
The next action is recommended.
The human sees the reasoning.
The human approves.
The system performs the appropriate next step.
The result is recorded.
The workflow learns.
That is the direction.
Less chasing.
Less repetition.
Less fragmentation.
More visibility.
More human control.
What We Should Stop Calling Innovation
Here are a few things I would stop celebrating.
Another dashboard
Unless it eliminates work.
Another portal
Unless it reduces fragmentation.
Another chatbot
Unless it solves a meaningful workflow problem.
Another AI feature
Unless it produces measurable value.
Another automation
Unless it removes something humans shouldn't have to do.
Healthcare does not need more technology theater.
It needs operational outcomes.
What Physicians Should Demand From AI Vendors
Before buying anything, ask:
"What exactly does this eliminate?"
Not:
What does it do?
Ask:
What does it remove?
Then ask:
How many minutes does it save?
What happens when it is wrong?
Can I see why it made the recommendation?
Who remains accountable?
Can my staff override it?
Can I audit it?
Does it integrate with my existing workflow?
What happens to my data?
What measurable outcome should improve?
If the vendor cannot answer those questions clearly, be
cautious.
The Metrics That Matter
Forget vanity metrics.
Track outcomes.
Clean claim rate
Denial rate
Denial recovery rate
Days in A/R
A/R aging
Time to resolution
Staff hours spent on rework
Manual touches per claim
First-pass acceptance
Net collection rate
Physician administrative hours
And one metric I would add:
Time returned to clinicians.
Because that is the point.
The Statistics Behind the Problem
The numbers reinforce the human story.
The American Hospital Association reported that hospitals
spent more than $43 billion in 2025 trying to collect payments from
insurers for care that had already been provided. (trustees.aha.org)
The AHA also reported that nearly 90% of physicians say
prior authorization increases physician burnout to some degree. (trustees.aha.org)
And the AHA's 2026 environmental scan found that 57% of
physicians identify administrative burden as the biggest opportunity for AI.
(aha.org)
These numbers are not just operational statistics.
Behind every hour of administrative work is a person.
Behind every delayed payment is a practice.
Behind every overloaded physician is a human being.
Behind every healthcare transaction is a patient.
Recent News: The Human Side of Healthcare Is Still the
Story
The Malachi LeBlanc story appeared this week amid a
healthcare news cycle dominated by larger institutional developments.
But Malachi's story reminds us that healthcare is ultimately
experienced one human being at a time.
His parents, Grace LeBlanc and Myron LeBlanc, faced a
devastating outcome and chose to help another family through organ donation.
That is not a technology story.
It is not a policy story.
It is not a corporate story.
It is a human story.
And perhaps that is exactly why it deserves our attention.
Because the more technology we introduce into healthcare,
the more important it becomes to remember what healthcare is actually for.
People.
The Ethical Question
There is a question healthcare AI founders should ask before
every automation project:
If this works perfectly, what human time does it give
back?
Then ask the harder question:
What happens if it doesn't?
Good healthcare AI needs both answers.
The ethical framework should include:
Privacy
Security
Transparency
Human oversight
Auditability
Appropriate escalation
Bias monitoring
Data governance
Compliance
Patient protection
Automation should never become an excuse to stop thinking.
The Legal Question
Billing automation exists within a complicated legal and
regulatory environment.
Practices and vendors should consider:
HIPAA and privacy obligations
Business associate arrangements
Security controls
Access permissions
Audit trails
Coding requirements
False Claims Act considerations
Payer contracts
Documentation requirements
State-specific rules
The exact obligations depend on the system and use case.
But one principle is universal:
AI does not eliminate accountability.
If a human organization uses an AI recommendation, it
remains responsible for establishing appropriate controls around that use.
Five AI Mistakes I Would Avoid
Mistake 1: Starting With Technology
Start with the workflow.
Mistake 2: Automating Everything
Automate selectively.
Mistake 3: Ignoring Exceptions
Exceptions are where healthcare becomes difficult.
Design for them.
Mistake 4: Removing Humans From High-Stakes Decisions
Human oversight should be intentional.
Mistake 5: Measuring AI Activity
Measure business and clinical impact instead.
A Practical 30-Day Challenge for Clinic Owners
If you want to explore AI without launching a massive
transformation project, try this.
Week 1: Observe
Pick one workflow.
Watch what staff actually do.
Don't rely on policy manuals.
Week 2: Measure
Count:
Touches.
Minutes.
Errors.
Rework.
Delays.
Week 3: Redesign
Ask:
What can we eliminate?
What can we simplify?
What can we standardize?
What can AI assist with?
Week 4: Pilot
Test one small intervention.
Measure the outcome.
Then decide whether to expand.
That's it.
No giant transformation program required.
The Most Dangerous Phrase in Healthcare
I think one of the most dangerous phrases in healthcare is:
"That's just how we do it."
It sounds harmless.
It isn't.
Every inefficient process began somewhere.
Someone created it.
Someone added a step.
Someone added an exception.
Someone added another exception.
Years later, nobody remembers why it exists.
But everyone is still doing it.
AI gives us an opportunity to ask:
Does this step still need to exist?
That may be more valuable than asking whether AI can perform
it.
The Future of Medical Billing Isn't More Billing
It is less unnecessary billing work.
That distinction matters.
The future should look less like:
Human → portal → spreadsheet → payer → email →
spreadsheet → portal
and more like:
Data → intelligence → recommendation → human approval →
action
The machine handles repetition.
The human handles judgment.
The practice sees the result.
That is a healthier division of labor.
The Bigger Healthcare Innovation Opportunity
We often talk about AI as if the biggest opportunity is
replacing intelligence.
I think the bigger opportunity is amplifying human
intelligence.
Let machines search.
Let machines compare.
Let machines monitor.
Let machines classify.
Let machines remind.
Let machines identify patterns.
Then let humans decide what those patterns mean in context.
That is particularly important in healthcare.
Because context is everything.
Why Independent Practices Should Pay Attention
Independent practices cannot afford unlimited administrative
overhead.
Every unnecessary process consumes scarce resources.
That makes workflow automation particularly important for
small and medium-sized practices.
The goal isn't to become a technology company.
The goal is to remain a great medical practice without
allowing administrative complexity to overwhelm it.
That is a very different objective.
The Question I Would Ask Every Physician
Forget the AI hype for a minute.
Forget the vendor demos.
Forget the buzzwords.
Ask yourself:
What part of my day would disappear if I redesigned my
practice from scratch today?
That answer is probably where your best automation
opportunity is hiding.
And it may not be the most sophisticated workflow.
It may be something incredibly boring.
That is okay.
Because boring problems can have enormous value.
Three Takeaways
1. Don't automate before you simplify.
Eliminate. Simplify. Standardize. Automate.
2. Measure time, not just dollars.
Physician time is a healthcare resource.
3. Keep humans where humans matter.
AI should handle repetition. Humans should handle
judgment.
Final Thoughts: The Patient Is Still the Point
The story of Malachi LeBlanc, Grace LeBlanc and Myron
LeBlanc is not a story about technology.
It is a story about love.
It is about an impossible decision.
It is about what people do when medicine can no longer give
them the outcome they desperately wanted.
And it reminds us why healthcare exists.
Not for the claim.
Not for the code.
Not for the dashboard.
Not for the payer portal.
Not for the AI model.
For the person.
That is why I am interested in healthcare automation.
Not because I think machines are the future of medicine.
Because I think human attention is too valuable to waste
on work machines can safely help perform.
The future of healthcare should not be:
AI versus humans.
It should be:
AI for the work humans don't need to do, so humans have
more time for the work only humans can do.
That is the future worth building.
Get Involved: Don't Just Read This
Here is my challenge to physicians and clinic owners:
What is the most ridiculous repetitive administrative
task you still have to perform in your practice?
Don't give me the polished answer.
Give me the real one.
The task your staff hates.
The task everyone complains about.
The task you've automated three times but still have to
touch.
Tell me in the comments.
Then share this article with one physician or clinic owner
who is quietly dealing with the same problem.
And if you believe physician time should be spent on patients
rather than preventable administrative friction, repost this conversation.
Maybe the next important healthcare innovation isn't another
technology.
Maybe it is simply deciding:
What should we stop doing?
Frequently Asked Questions
Is AI going to replace physicians?
That is the wrong starting question.
The more useful question is which tasks AI can safely assist
with while preserving physician judgment and patient relationships.
Will AI replace medical billing staff?
The more realistic near-term model is augmentation.
AI can perform repetitive analysis and workflow support
while humans handle exceptions, judgment and accountability.
Can AI eliminate denials?
No responsible technology should promise that.
It may help identify patterns, prevent certain errors and
prioritize work.
But payer behavior and healthcare complexity mean some
denials will remain.
Should every medical practice use AI?
No.
A practice should adopt technology when there is a clearly
defined problem, measurable opportunity and appropriate governance.
What should practices automate first?
Start with a workflow that is repetitive, measurable and
costly.
How do we know whether AI is working?
Measure outcomes.
Look at denial rates, A/R, staff time, rework, payment
velocity and physician administrative time.
What is the biggest AI mistake healthcare organizations
make?
Starting with the technology instead of the problem.
What does human-in-the-loop mean?
It means AI can assist with analysis or recommendations
while a human retains responsibility for appropriate decisions.
Myth Busters
Myth: More automation equals better healthcare.
False.
Poorly designed automation can create new problems.
Myth: AI must be autonomous to be valuable.
False.
An AI system that makes a useful recommendation and knows
when to escalate can be extremely valuable.
Myth: Administrative work is separate from patient care.
False.
Administrative systems influence practice sustainability,
clinician workload and the ability to provide care.
Myth: Technology automatically improves workflows.
False.
Technology can make a bad workflow faster.
Myth: Physician time is simply a labor expense.
False.
Physician attention is one of the most valuable resources in
healthcare.
Tools, Metrics and Resources
For practices exploring workflow automation, start with:
Workflow mapping tools to document processes.
Revenue-cycle dashboards to establish baselines.
Denial analytics to identify recurring failure
patterns.
EHR and practice-management integrations to reduce
duplicate data entry.
AI-assisted document review for appropriate
repetitive tasks.
Human approval workflows for higher-risk actions.
Audit logs for accountability.
Security and compliance assessments before deploying
AI against protected health information.
Most importantly:
Use the tools you already have before buying more tools.
The objective is not a larger technology stack.
The objective is a smaller workload.
Future Outlook
Healthcare AI is moving from isolated assistants toward workflow
intelligence.
The distinction is important.
A chatbot answers.
A workflow system acts.
The next generation of healthcare automation will
increasingly connect:
Information
→
Reasoning
→
Recommendation
→
Human approval
→
Action
→
Measurement
That model has enormous potential in revenue-cycle
management.
The future may be less about asking:
"What did the payer do?"
and more about:
"What is likely to happen next?"
Less:
"Why did this claim fail?"
and more:
"How can we prevent similar failures?"
Less:
"Where is this information?"
and more:
"Here is the information you need."
That is the difference between software that stores
healthcare information and software that helps people work with it.
About the Author
Dr. Daniel Cham is a physician, medical consultant
and healthcare entrepreneur focused on the intersection of medical
technology, healthcare management and medical billing.
As founder of OnnX, he is focused on practical
applications of AI and workflow automation that can help small and medium-sized
medical practices reduce unnecessary administrative work, improve revenue-cycle
operations and protect clinician time.
His approach is straightforward:
Understand the workflow.
Find the friction.
Eliminate unnecessary work.
Automate what can be safely automated.
Keep humans in control where judgment matters.
Connect
with Dr. Daniel Cham on LinkedIn
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Disclaimer
This article is provided for general educational and
informational purposes only. It does not constitute medical, legal,
compliance, coding, financial or professional advice.
Healthcare organizations should consult appropriately
qualified professionals regarding their specific clinical, legal, regulatory,
privacy, security, compliance and revenue-cycle circumstances.
References
1. KSAT — The Malachi LeBlanc story. Reporting on
2-year-old Malachi LeBlanc, his parents Grace and Myron LeBlanc, and their
decision to pursue organ donation after his death.
2. American Hospital Association — 2026 Environmental
Scan. Current analysis of healthcare trends, including physician views of
administrative burden and AI opportunities.
Read
the AHA Environmental Scan
3. American Hospital Association — Administrative burden
and healthcare costs. Analysis highlighting the financial and operational
consequences of payer-related administrative work.
Final Invitation
Question the workflow.
Protect physician time.
Build technology that gives human beings more room to
care for other human beings.
If this perspective resonates with you, leave a comment,
share your experience and repost this article so more physicians and clinic
owners can join the conversation.
The future of healthcare should not be about replacing
humans.
It should be about removing the unnecessary work that
keeps humans from doing what they do best.
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#RevenueCycleManagement #PhysicianBurnout #AdministrativeBurden
#HealthcareInnovation #MedicalPractice #PrivatePractice #ClinicOwners
#PhysicianEntrepreneur #HealthTech #DigitalHealth #WorkflowAutomation #HealthcareTechnology
#PatientCare #PhysicianLeadership #HealthcareOperations #AIinHealthcare
#RevenueCycle #MedicalPracticeManagement #HealthcareTransformation #OnnX
