A father. A son. A kidney transplant. A basketball championship. And a healthcare story we keep losing.
“Commodifying such micro-interactions undermines the
value of having a trusted personal doctor.” — Ishani
Ganguli, MD, associate professor of medicine at Harvard Medical School,
The Washington Post
A kidney saved a life. But healthcare still struggles to
preserve the story behind it.
Mitch Phillis Gave His Father a Kidney.
Then he went to Orlando to shoot basketballs.
That sounds like the beginning of a joke.
It isn't.
It is the story of Mike Phillis, his son Mitch
Phillis, Mitch's wife Susan Phillis, transplant surgeon Amit
Tevar, MD, and a medical journey that began in Salem, Ohio, passed
through UPMC Montefiore in Oakland, Pittsburgh, made a stop in Chicago,
and somehow ended with a national championship in Orlando, Florida.
And it raises a question that has almost nothing to do with
basketball.
Why is healthcare so good at recording what happened and
still so bad at remembering why it mattered?
That question matters to physicians.
It matters to clinic owners.
It matters to billers.
It matters to patients.
And increasingly, it matters to anyone building artificial
intelligence for healthcare.
Because Mitch and Mike Phillis did not experience their
lives as a series of disconnected database fields.
Neither do your patients.
It Started With a Blood Donation
Mike Phillis wasn't looking for a kidney problem.
He was trying to donate blood.
He was turned away because his blood pressure was too high.
That ordinary moment led doctors to discover that his kidney
function had fallen to about 34 percent.
For years, Mike's kidney function continued to decline.
There wasn't a neat explanation.
There wasn't one dramatic event that made everything
obvious.
There was simply a slow deterioration that eventually became
impossible to ignore.
By October 2025, his kidney function was around 20
percent.
By December, he was eligible to begin the transplant
process.
And now the family had a problem that healthcare knows very
well:
They needed the right answer.
Fast.
Several of Mike's siblings wanted to donate.
They couldn't.
Then Mitch stepped forward.
Mitch was 41.
He lived in North Canton, Ohio.
He had a wife, Susan.
He had a life.
He had his own plans.
And he had something his father desperately needed.
A kidney.
Mike didn't exactly celebrate the idea.
He worried about his son.
His response was essentially:
I don't want to take something from your future to extend
mine.
Mitch apparently had a different definition of family.
If he could safely donate, he would.
Testing showed he was an excellent match.
So father and son headed to UPMC Montefiore in Pittsburgh.
That is where Amit Tevar, MD, surgical director of
UPMC's Kidney and Pancreas Transplant Program, and the transplant team entered
the story.
Mitch gave Mike one of his kidneys.
Mike received it.
The surgery worked.
And then something happened that healthcare documentation
does not always capture very well.
Life continued.
The Kidney Was the Beginning, Not the Ending
Mike's kidney function improved dramatically after the
transplant.
Mitch had to recover from donating an organ.
That alone would have been enough of a story.
But Mitch had another problem.
He apparently had too much time on his hands.
Enter:
Pop-A-Shot.
If you don't know what Pop-A-Shot is, imagine taking the
least complicated part of basketball and becoming completely unreasonable about
it.
No defense.
No coach.
No referees.
No trade rumors.
Just a basketball arcade machine and a clock.
Mitch had loved the game since childhood.
When he was about 10 or 11, he had used money from
delivering newspapers to buy a small Pop-A-Shot machine for his bedroom.
That childhood obsession returned during his recovery.
He saw the national competition.
He entered.
He qualified.
He went to Chicago.
At the 2026 Chicago Super Qualifier, Mitch—competing
as MAP21—beat THE-GLOVE, Gary Preston, 160-144 in the final and
secured his place in the national championship. Pop-A-Shot describes him as a
dark horse who upset JSCHWAB on his way to the championship.
Then came Orlando.
And ESPN.
And his father.
Four months after donating a kidney, Mitch Phillis was
standing in front of a basketball arcade game in Orlando.
His father, Mike, was there.
Mitch won.
The kidney donor became a national champion.
There is something almost delightfully absurd about that
sentence.
And something deeply human about it.
The Real Story Isn't the Kidney
It isn't even the basketball.
It is the connection between the two.
Mike's kidney disease was not an isolated laboratory value.
Mitch's donation was not an isolated procedure.
The transplant was not an isolated encounter.
The championship was not an isolated sporting event.
They were parts of one human story.
But healthcare systems tend to break stories into events.
Encounter.
Diagnosis.
Procedure.
Authorization.
Documentation.
Code.
Claim.
Payment.
Denial.
Each one can be perfectly documented.
And the whole story can still disappear.
That is the contradiction.
Healthcare Has a Memory Problem
We talk constantly about healthcare's data problem.
I think that description is incomplete.
We don't have a shortage of data.
We have an enormous amount of it.
We have laboratory results.
Clinical notes.
Imaging.
Medications.
Diagnoses.
Procedure codes.
Eligibility information.
Authorization records.
Referral records.
Claims.
Remittance advice.
Messages.
Phone calls.
Scanned documents.
Portals.
Faxes.
And, somewhere in there, the patient.
The problem is not that healthcare has no memory.
The problem is that healthcare has fragmented memory.
Everyone remembers something.
Nobody necessarily remembers everything together.
The physician knows why the decision was made.
The authorization team knows what the payer required.
The biller knows what the claim said.
The patient knows what actually happened.
The payer knows what its system received.
And then everyone wonders why the story doesn't line up.
The Patient Is Not the Chart
Here is where I would challenge conventional healthcare
technology thinking.
The patient is not the chart.
The chart is a representation.
A useful one.
An imperfect one.
Sometimes a very good one.
But still a representation.
The diagnosis is not the patient.
The code is not the encounter.
The authorization is not the clinical reasoning.
The claim is not the care.
And the denial is not necessarily the explanation.
A claim can be perfectly formatted and still fail to carry
the context that made the care make sense.
That is why I don't think the future of healthcare billing
is simply about building better downstream tools.
We need to ask a more uncomfortable question:
Why are we waiting until the claim fails to discover that
the information was incomplete?
The Industry Loves the Rearview Mirror
Healthcare has become remarkably sophisticated at fixing
problems after they happen.
Denial management.
Claims scrubbing.
Appeals.
Coding audits.
Payment reconciliation.
Revenue-cycle analytics.
Artificial intelligence that reviews charts after the
encounter.
Artificial intelligence that finds missing documentation
after the claim.
Artificial intelligence that writes the appeal after the
payer says no.
All of that has value.
But there is a strange assumption underneath it:
The problem has already happened.
Then we optimize the cleanup.
Imagine doing that in an airport.
The plane takes off.
Then someone asks:
“Did we actually put fuel in it?”
That is not an automation problem.
That is a timing problem.
And healthcare has plenty of those.
What If the Denial Is Actually a Clue?
I have become increasingly skeptical of the idea that the
denial is the enemy.
A denial can be useful.
It can tell us something.
Maybe eligibility wasn't captured correctly.
Maybe authorization information didn't travel with the
encounter.
Maybe the referral wasn't connected.
Maybe documentation was technically present but
operationally inaccessible.
Maybe the physician's reasoning never became structured
information.
Maybe the information existed in one system but never
reached the next.
The denial is what we see.
The information failure may have happened much earlier.
So instead of asking:
“How do we fight this denial?”
I'd also ask:
“Where did the story first become ambiguous?”
That question moves the conversation upstream.
And upstream is where the leverage may be.
Mitch and Mike Give Us a Better Definition of an Outcome
Suppose we reduced the Phillis story to a medical dashboard.
Mike:
Kidney disease.
Mitch:
Living donor.
Procedure:
Kidney transplant.
Outcome:
Improved kidney function.
Done.
Case closed.
Except it isn't.
Because months later:
Mike is alive.
Mitch is competing.
They are traveling together.
They are watching basketball.
They are standing in Orlando.
Mitch wins.
Mike gets to watch his son win.
That is an outcome too.
It just doesn't fit neatly into a claim field.
This is one of the great weaknesses of modern healthcare
measurement.
We are extremely good at measuring events.
We are less consistent at preserving meaning.
Medicine Can Move an Organ.
Why Can't It Move Context?
This may be the most provocative question in the entire
story.
Modern medicine can do something extraordinary.
A kidney can be removed from one living human being.
Transported into another.
Connected to the recipient's circulation.
And, if everything goes well, it begins doing its job.
Think about that.
Healthcare can move an actual organ from one human being to
another.
Yet we still struggle to move a piece of information from
one department to another.
The kidney travels better than the context.
That should bother us.
A lot.
The Fax Machine Would Like a Word
This is where healthcare gets unintentionally funny.
We can transplant kidneys.
We can operate robots.
We can sequence DNA.
We can train artificial intelligence models on enormous
amounts of information.
And then someone says:
“Can you fax that?”
Apparently the future arrived.
It just forgot the fax number.
The humor is useful because the problem isn't really the
fax.
The fax is a symptom.
The deeper problem is that healthcare has accumulated layers
of technology without necessarily redesigning the information architecture
underneath them.
We keep adding tools.
We rarely ask whether the story is becoming easier to
follow.
This Is Where AI Could Actually Matter
I'm not particularly interested in AI because it can write
another note.
Healthcare already has plenty of notes.
I'm interested in AI that can help preserve context.
That's different.
Could an AI system recognize that an authorization decision
today matters to a claim weeks later?
Could it understand that a referral is connected to a
specific clinical pathway?
Could it recognize that the same diagnosis can mean
different things in different encounters?
Could it identify missing context before the claim reaches
the payer?
Could it explain where a piece of information came from?
Could it tell a biller:
“This claim appears incomplete because the authorization
context captured earlier has not propagated to the current encounter.”
That is more interesting than:
“Would you like me to summarize the chart?”
We have enough summaries.
We need better continuity.
Human-Supervised AI Is More Interesting Than AI Alone
There is another reason I'm cautious about the industry's
obsession with autonomous AI.
Healthcare isn't a clean data environment.
It is messy.
Patients are messy.
Documentation is messy.
Workflows are messy.
Exceptions are everywhere.
That makes human judgment important.
The goal shouldn't necessarily be:
Remove the human.
It should be:
Remove the unnecessary reconstruction so the human can
focus on judgment.
Let AI find patterns.
Let AI surface missing context.
Let AI connect related information.
Let AI flag inconsistencies.
Let AI explain why something may fail.
But let qualified humans decide when the stakes require
judgment.
That's not anti-AI.
It is a more mature view of AI.
What Dr. Ishani Ganguli's Argument Gets Right
Dr. Ishani Ganguli recently wrote about the growing practice
of charging patients for messages to their physicians.
Her concern wasn't that physician time has no value.
Quite the opposite.
The work matters.
The problem is what happens when every interaction is
reduced to a transaction.
Her broader point is that primary care works partly because
physicians develop continuity and knowledge of the patient.
That matters here.
Because continuity is not just a clinical luxury.
It is an information advantage.
A physician who knows the patient doesn't merely know more
facts.
They understand relationships between facts.
That is context.
And context is incredibly difficult to recreate after it has
been lost.
The Hidden Enemy Isn't Data.
It's Disconnection.
Think about what happens in a typical practice.
The patient tells the receptionist something.
The receptionist records part of it.
The nurse sees something else.
The physician documents something else.
The authorization team needs another piece.
The biller interprets the documentation.
The payer evaluates the claim.
Then the denial comes back.
Someone says:
“What happened?”
That may be the most expensive sentence in healthcare.
Because it means the organization has lost continuity.
Someone now has to reconstruct the story.
And reconstruction is expensive.
It consumes time.
It consumes attention.
It creates errors.
It creates frustration.
And sometimes it creates a denial.
The Real Cost of Lost Context
We usually calculate healthcare waste in dollars.
We should.
But there are other currencies.
Time.
Attention.
Trust.
Physician energy.
Staff morale.
Patient patience.
Every time a physician has to explain something twice, the
system spends attention.
Every time a biller searches through multiple systems, the
system spends attention.
Every time a patient repeats their history, the system
spends trust.
The invoice doesn't always show those costs.
The practice still pays them.
What Clinic Owners Should Ask Instead
If I were sitting with a physician-owned practice, I
wouldn't start by asking:
“Which AI platform do you use?”
I'd ask:
“Where does your staff spend time reconstructing what
happened?”
That's a better question.
Then I'd ask:
Where does information first get lost?
Not where the denial happens.
Earlier.
What information gets entered more than once?
Duplicate entry is often a clue that systems aren't
communicating.
What does the physician know that billing doesn't?
That gap matters.
What does billing know that the physician doesn't?
That gap matters too.
What does the patient have to explain repeatedly?
Listen carefully to that answer.
Which workflows depend on someone's memory?
Memory is wonderful.
It is also a terrible database.
Where does an authorization become disconnected from the
encounter?
That is where money and time can disappear.
How long does it take to reconstruct a patient's story?
Start measuring that.
Call it context recovery time.
You may discover that it is one of the most expensive
invisible metrics in the practice.
The Upstream Test
Here's a simple test for any healthcare workflow.
Take one patient.
Follow the journey.
Capture.
What was known at the beginning?
Structure.
Was the important information organized?
Preserve.
Did it survive the next handoff?
Propagate.
Did the relevant context move where it needed to go?
Act.
Did someone make the right decision using it?
Learn.
Did the system become better because of what happened?
If the answer breaks at any point, you have found a
potential failure point.
That is upstream intelligence.
Not more information.
Better information at the right time.
Pain → Solution → Proof
The Pain
The physician remembers the patient.
The system remembers fragments.
The biller sees the claim.
The payer sees the submitted data.
The patient remembers the entire experience.
Those are five different perspectives on one event.
The Solution
Build a canonical context record that preserves the
information that matters across the workflow.
Not another giant repository.
Not another dashboard.
Not another place for people to click.
A structured layer of context that can move with the
encounter.
The Proof
The Phillis story is an unusually beautiful example.
Mike's kidney disease had a history.
Mitch's decision had a reason.
The transplant had a purpose.
The recovery had a consequence.
The championship had a meaning.
None of those things existed independently.
The story was the connection.
Five Myths Healthcare Should Retire
Myth 1: More data means more intelligence.
No.
Sometimes more data simply gives everyone more places to
search.
Myth 2: A complete chart means a complete story.
No.
A chart can be comprehensive and still fail to communicate
the relationships between events.
Myth 3: A clean claim means clean upstream data.
Absolutely not.
The claim is the output.
It isn't necessarily evidence that the process was healthy.
Myth 4: AI will solve bad workflows.
AI can make bad workflows faster.
That isn't the same thing.
Myth 5: The answer to fragmentation is another
application.
Sometimes the answer is better architecture.
The Most Dangerous Phrase in Healthcare
It may be:
“That's in another system.”
Because those five words are an architectural confession.
The information exists.
But it isn't where the next person needs it.
Healthcare has become very good at creating islands of
information.
The future should be about bridges.
What I Would Change First
If a clinic asked me where to begin, I would not tell them
to buy an expensive AI system tomorrow.
I'd start smaller.
Step 1: Choose one high-friction workflow.
Eligibility.
Authorization.
Referral.
Coding.
Claims.
Pick one.
Step 2: Follow the information.
Not the patient.
The information.
Where does it start?
Where does it go?
Who touches it?
Who changes it?
Where does it disappear?
Step 3: Find the first manual reconstruction.
That's your clue.
Step 4: Identify the missing context.
What did the next person need but not have?
Step 5: Structure that information earlier.
Capture it before it becomes a scavenger hunt.
Step 6: Preserve it.
Don't let critical context live only in an email, fax, phone
call or someone's memory.
Step 7: Propagate it.
Make relevant context available at the next decision point.
Step 8: Automate carefully.
Use AI after the information architecture makes sense.
Step 9: Keep humans accountable.
Especially where clinical, financial or ethical judgment
matters.
Step 10: Measure what changed.
Track:
First-pass acceptance.
Preventable denials.
Authorization turnaround.
Eligibility errors.
Staff touches.
Duplicate entry.
Rework.
Patient callbacks.
Time spent searching.
And again:
Context recovery time.
The OnnX Thesis
This thinking is part of why I am building OnnX.
The premise is intentionally contrarian:
Healthcare billing is a data-quality problem before it
becomes a billing problem.
Most systems attack the visible problem.
The denial.
The rejected claim.
The missing code.
The appeal.
The unpaid balance.
But those are often downstream symptoms.
The more interesting question is:
What happened upstream?
Was the information captured correctly?
Was it structured?
Was it preserved?
Did it propagate?
Was the right person able to act on it?
Did the system learn?
That is the difference between reactive revenue cycle
management and upstream intelligence.
The goal isn't to eliminate humans.
It is to stop using highly trained humans as middleware.
Humans Should Not Be the API
This may be the most important operational point.
In too many healthcare workflows, the human becomes the
integration layer.
The physician explains something to the nurse.
The nurse explains it to authorization.
Authorization explains it to billing.
Billing explains it to the payer.
The payer sends something back.
Someone calls the physician.
The physician says:
“I already documented that.”
And everyone starts searching.
That's not intelligence.
That's a human-powered API.
Humans are extraordinarily expensive middleware.
We should stop designing systems that require them to
perform data integration manually.
What Mike and Mitch Teach Us About Outcomes
There is a temptation to turn every healthcare story into a
statistic.
This story resists that.
Mike was not merely a 67-year-old kidney recipient.
Mitch was not merely a 41-year-old living donor.
Susan was not merely a spouse.
Dr. Tevar was not merely a surgeon.
UPMC Montefiore was not merely a facility.
Salem was not merely a location.
North Canton was not merely an address.
Chicago was not merely a qualifier.
Orlando was not merely a championship venue.
Those details matter because humans live in context.
Take away all the context and you still have data.
But you don't have the story.
The Strange Beauty of Healthcare
Medicine is at its best when it remembers that the point
isn't the procedure.
The point is what the procedure allows someone to do
afterward.
A transplant is successful because someone gets another
chance at life.
A hip replacement matters because someone can walk.
Cancer treatment matters because someone gets another
birthday.
Primary care matters because someone can stay healthier
before the crisis.
Billing matters because the practice has the resources to
keep providing care.
These things are connected.
We've just built systems that often make them look separate.
The Future May Not Be More AI.
It May Be Better Context.
This is where I think healthcare technology needs a reset.
We have spent years asking:
How can AI do more?
Perhaps we should ask:
How can healthcare forget less?
That's a different question.
And potentially a much more important one.
AI should not simply generate.
It should understand provenance.
It should preserve relationships.
It should surface missing context.
It should identify contradictions.
It should know what it knows.
It should know what it doesn't know.
And when the stakes are high, it should know when to hand
the decision back to a human.
That's not artificial intelligence replacing healthcare.
That's intelligence helping healthcare become more coherent.
The Phillis Family Accidentally Gave Healthcare a
Metaphor
Mike needed a kidney.
Mitch gave him one.
The transplant changed Mike's future.
Then Mitch went to Chicago.
Then Orlando.
Then ESPN.
Then he won.
A system looking only at the medical record might see the
transplant.
A system looking only at the sports coverage might see the
championship.
A human being sees the connection.
The son who gave his father a kidney was the same son his
father later watched become a national champion.
That's the story.
And that's what healthcare systems often struggle to
preserve.
Not facts.
Relationships between facts.
A Final Challenge to Physicians and Clinic Owners
Tonight, pick one patient.
Not your easiest patient.
Pick an ordinary complicated one.
Ask your team to reconstruct the patient's journey from the
first contact through the claim.
Don't tell them where to look.
Just ask:
“Tell me what happened.”
Then watch.
Watch how many systems they open.
Watch how many times they say:
“I'm not sure.”
Watch how often someone says:
“That's in another system.”
Watch how many times someone has to call somebody else.
Watch how much information is reconstructed from memory.
Then ask the question that matters:
Why did we make humans reconstruct a story that our
system already experienced?
That is where your next improvement opportunity may be
hiding.
Not in another dashboard.
Not in another chatbot.
Not in another denial-management queue.
Upstream.
The Question I Can't Stop Thinking About
Mitch Phillis gave his father Mike a kidney.
Four months later, Mike was in Orlando watching his son
compete for a national championship.
Think about what had to remain connected for that moment to
happen.
A diagnosis.
A declining kidney.
A donor.
A father.
A son.
A wife.
A transplant team.
A surgery.
A recovery.
A trip.
A basketball machine.
A championship.
Life doesn't care which department owns each piece.
Life is one story.
Healthcare shouldn't require patients to become the
integration layer between its departments.
And it shouldn't require physicians and billers to become
detectives every time information crosses a boundary.
The kidney made it from Mitch to Mike.
Maybe healthcare should learn to move context that well.
Final Question
Where does your patient's story first disappear inside
your practice?
Is it at eligibility?
Authorization?
Documentation?
Coding?
Claims?
Denials?
Or somewhere even earlier?
Don't tell me where the denial happened.
Tell me where the story broke.
That's probably where the real problem started.
If this perspective resonates, consider reposting it to help
other physicians and clinic owners rethink how information moves through their
practices.
Frequently Asked Questions
Is this really a medical billing problem?
Not always.
Billing may simply be where an upstream information problem
becomes visible.
Why call it a context problem?
Because healthcare can possess all the relevant facts while
losing the relationships between those facts.
Does artificial intelligence solve that?
Only if the underlying information is structured well enough
for AI to work with it responsibly.
Should humans be removed from the process?
No.
Humans should spend less time reconstructing information and
more time applying judgment.
What is upstream intelligence?
It is the practice of capturing, structuring, preserving and
propagating relevant context before downstream problems occur.
What is context recovery time?
The amount of human time required to reconstruct what
happened, why it happened and what should happen next.
Why does this matter financially?
Because lost context can create rework, delays, preventable
denials, staff burden and patient frustration.
Why does this matter clinically?
Because fragmented information can interfere with
continuity, coordination and decision-making.
Myth Buster: The Biggest One
“The information is somewhere in the chart.”
That may be technically true.
It may also be completely useless.
Information that cannot be found, interpreted or propagated
at the moment of decision is functionally unavailable.
Healthcare needs to stop confusing storage with accessibility.
And stop confusing accessibility with understanding.
Practical Metrics for the Next Generation Practice
A modern clinic should consider measuring more than revenue.
Measure:
Context recovery time
Number of systems accessed per claim
Duplicate data-entry events
Authorization rework
Preventable denials
Eligibility-related denials
Staff touches per claim
Patient callbacks
Documentation clarification requests
Time from clinical decision to complete administrative
context
These metrics reveal something conventional revenue-cycle
reports often miss:
how much friction exists between what the physician knows
and what the system knows.
Ethical Considerations
The more healthcare depends on AI, the more important
provenance becomes.
If an AI system recommends an action, people should be able
to ask:
Where did that information come from?
Was it current?
Who verified it?
What context was missing?
What assumptions did the system make?
Who made the final decision?
Healthcare should not trade human judgment for algorithmic
opacity.
The goal should be traceable intelligence.
The Future
I don't think healthcare needs another decade of simply
adding technology to broken workflows.
We need to become more selective.
Less:
“What else can we automate?”
More:
“What should never have required manual reconstruction?”
Less:
“How do we fix this denial?”
More:
“Why did this become deniable?”
Less:
“Where is the data?”
More:
“Does the right person have the right context at the
right moment?”
That is the shift from automation to intelligence.
And it is a shift worth making.
About the Author
Dr. Daniel Cham is a physician, medical consultant
and healthcare entrepreneur focused on the intersection of clinical medicine,
healthcare operations, medical technology and medical billing.
His work examines how better information architecture,
upstream intelligence and human-supervised artificial intelligence can help
physicians and clinic owners navigate increasingly complex healthcare
workflows.
Connect with Dr. Cham on LinkedIn to
learn more.
He is the founder of OnnX, an AI-powered healthcare
billing concept built around a simple premise:
Healthcare billing problems often begin as
information-quality problems upstream.
His perspective draws on experience in clinical medicine,
healthcare management, medical consulting and medical billing.
Continue the Conversation
Healthcare technology will continue to evolve.
Artificial intelligence will become more capable.
Automation will become more sophisticated.
But the fundamental question will remain remarkably human:
Can the system remember the person while it processes the
data?
That may be one of the defining questions of the next
generation of healthcare.
Knowledge drives progress. Start your journey here.
Check the Featured section of my LinkedIn profile for
a free resource. No signup required there.
Listen to the
podcast on Spotify
Subscribe and
watch on YouTube
If this perspective resonates, consider reposting it to help
other physicians and clinic owners rethink how information moves through their
practices.
Disclaimer
This article is for general educational and informational
purposes only. It is not medical, legal, financial or professional advice.
Healthcare organizations and professionals should evaluate their own clinical,
operational, compliance and technology requirements with appropriate qualified
advisers.
The line I would leave readers with
The patient went home.
The story should have gone with them.
And if it didn't, perhaps the problem wasn't that healthcare
needed more data.
Perhaps it needed a better memory.
References
- Pittsburgh
Post-Gazette — Roberta Burkhart
“From kidney transplant to ESPN, father and son score remarkable victories.” October 4, 2026. The primary source for the Mitch and Mike Phillis story, transplant, recovery, and Pop-A-Shot journey.
Pittsburgh Post-Gazette article - Pop-A-Shot
— 2026 National Championship
Official tournament information documenting Mitch Phillis as the 2026 National Champion and his path from the Chicago Super Qualifier to the national championship.
Pop-A-Shot 2026 National Championship - Pop-A-Shot
— Official Championship Recap
Details the August 2026 championship in Orlando, including Mitch's 3–1 victory over James Isham and his 95.3% shooting accuracy.
Pop-A-Shot Championship Recap - The
Washington Post — Ishani Ganguli, MD
“Bill patients for emailing? This could backfire.” October 1, 2026. Ganguli, an associate professor of medicine at Harvard Medical School, discusses how fragmented interactions can undermine the continuity and relationship at the heart of primary care.
The Washington Post article
#Healthcare #MedicalBilling #HealthcareAI #HealthTech
#PhysicianFounder #MedicalPractice #HealthcareInnovation
#RevenueCycleManagement #HealthcareOperations #ClinicalWorkflow
#PriorAuthorization #HealthcareData #UpstreamIntelligence #IndependentPhysicians
#ClinicOwners #PatientExperience #MedicalTechnology #DigitalHealth #OnnX
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