What a firefighter in Michigan, a football coach in Houston, and a denied medical claim have in common: the outcome happens downstream, but the story usually starts upstream.
“What guides us in our conversations, especially around AI, is: How does it link back to the mission, and are we using it for good?” — Yvonne Hao, COO and General Partner, Flagship Pioneering, speaking at an Axios health event in Boston on September 10, 2026.
The woman who saved a stranger’s life had never met him.
Donn Landholm was sitting in Houston waiting for news that could determine whether he had a future.
He had been diagnosed with chronic myelomonocytic leukemia, or CMML-1, a rare blood cancer that begins in the bone marrow.
He had already gone through five rounds of chemotherapy at MD Anderson.
But chemotherapy wasn’t enough.
For long-term survival, doctors told him he needed a stem-cell transplant.
His family was tested.
His youngest daughter, Kate, was a 50% match.
His brother and sister were also tested.
None was a suitable donor.
Then the phone call came.
“We have a match.”
The donor was a woman in the United States.
That was all Landholm knew.
Her name was Morgan Chalup.
She lived nearly 1,400 miles away in Empire, Michigan, a tiny village on the shores of Lake Michigan.
She was a firefighter and EMT with the Glen Lake Fire Department.
She was also a mother of two.
And three years earlier, she had joined the stem-cell donor registry after her father-in-law, Jeff, was diagnosed with leukemia.
She had no idea whether she would ever be called.
Then one day, while driving her daughters home from sports practice, her phone buzzed.
She was a match.
Her first thought?
Spam.
Then she called the number.
It wasn’t spam.
Someone needed her cells.
And she already knew her answer.
“I was going to donate,” she said. “It was never a question for me, yes or no.”
On January 8, 2025, Chalup donated peripheral blood stem cells at the Versiti Blood Center of Michigan in Grand Rapids.
On February 4, Landholm received them at MD Anderson.
He spent 18 days isolated in his hospital room.
His wife, Cindi Landholm, created a wall of cards, letters and messages called “Donn’s Wall of Hope.”
His University of Houston football colleagues stood outside the hospital holding signs.
Landholm eventually went home.
He remained isolated for another 100 days.
Nineteen months after the transplant, he was back on the University of Houston sideline.
A bone marrow biopsy confirmed he had been in remission for six months.
Then something extraordinary happened.
The stranger became a person.
After the required waiting period, Landholm contacted the transplant program.
He wrote Morgan an email.
He rewrote it.
Then rewrote it again.
His wife thought it sounded too clinical.
She told him to add some adjectives.
That detail might be my favorite part of the entire story.
Because even after surviving leukemia, undergoing chemotherapy and receiving a life-saving transplant, Donn Landholm apparently still needed help writing the thank-you email.
Healthcare is nothing if not human.
Eventually, he pressed send.
Morgan replied.
They began exchanging photographs and messages.
Their families connected.
The strangers became friends.
And the entire chain began with one woman entering a registry years before she knew anyone would need her.
That is where this story gets interesting for physician owners.
Because the transplant isn’t actually the most interesting part.
The Most Important Part Happened Before Anyone Needed Saving
We tend to focus on the intervention.
The transplant.
The surgery.
The medication.
The procedure.
The claim.
The appeal.
The recovered revenue.
But the intervention is often just the visible end of a much longer chain.
Morgan Chalup’s donation happened because:
Someone recruited her.
She registered.
Her information was stored.
The system could search it.
A match could be identified.
She could be contacted.
She could donate.
The cells could be transported.
The clinical team could use them.
Landholm could receive them.
The system worked because the necessary information existed before the emergency.
That is a profoundly important idea.
And it has almost nothing to do with stem cells.
It has everything to do with healthcare.
Now Think About Your Last Denial
A patient comes into your clinic.
The front desk collects information.
Someone checks insurance.
The patient sees the physician.
The physician documents the encounter.
Someone codes it.
Someone submits the claim.
Then the payer says:
Denied.
Now everybody gets excited.
The biller opens the chart.
The practice manager starts asking questions.
Someone emails the physician.
The physician receives the message three days later.
Someone calls the payer.
Someone uploads another document.
Someone resubmits the claim.
Everyone works very hard.
Eventually, perhaps, the claim gets paid.
Then everyone moves on.
Until the next one.
Here’s the uncomfortable question:
Why did we wait until the claim failed to become interested in the information?
The Denial Is Not the Beginning
A denial is a visible event.
It is not necessarily the beginning of the problem.
The problem may have started:
At scheduling.
During registration.
During eligibility verification.
During authorization.
During documentation.
During coding.
During a handoff.
During a data transfer.
During a conversation nobody documented.
The claim simply became the place where the hidden problem finally became visible.
That is why I think the healthcare industry sometimes asks the wrong question.
We ask:
“How do we work denials faster?”
I think we should also ask:
“Why are we manufacturing denials in the first place?”
That is a much less comfortable question.
It is also potentially much more valuable.
Healthcare Has Become Very Good at Cleaning Up Its Own Mess
This may be one of the strangest characteristics of modern healthcare.
We have built extraordinary capabilities around correction.
Correct the note.
Correct the code.
Correct the claim.
Correct the authorization.
Correct the demographic information.
Correct the eligibility.
Correct the documentation.
Appeal.
Resubmit.
Repeat.
Healthcare has become exceptionally sophisticated at saying:
“No problem. We’ll fix it.”
But what if the problem is that we keep needing to fix the same kinds of things?
At some point, “we’ll fix it” stops being resilience.
It becomes a business model.
The Heroic Biller Problem
Every independent practice has one.
Maybe it’s Susan.
Maybe it’s Maria.
Maybe it’s Mike.
Maybe it’s the person who has been there for 14 years and knows exactly which payer portal to use, which physician tends to forget which field, which authorization number lives in which inbox, and which payer representative will actually answer the phone.
Everyone loves this person.
They should.
They’re probably holding half the practice together.
But here’s the problem.
If your revenue cycle depends on one employee remembering everything, you haven’t created a reliable system.
You’ve created institutional memory with a payroll number.
That person isn’t the problem.
The dependency is.
And when that person takes vacation?
Suddenly everyone discovers how much healthcare infrastructure was stored inside one human brain.
We Call This “Workflow”
Sometimes it isn’t.
Sometimes it’s just organized improvisation.
One employee checks one screen.
Another checks another.
The physician remembers something.
The biller sends a message.
The administrator calls somebody.
The claim gets fixed.
Everyone goes home.
Tomorrow, we do it again.
There is a strange tendency in healthcare to call this workflow because “workflow” sounds more sophisticated than:
“We have developed a very elaborate way of compensating for missing information.”
The Contrarian Idea
Here is my contrarian view:
The future of medical billing may have less to do with billing.
That sounds ridiculous.
Until you think about it.
The best billing workflow is arguably the one that creates fewer billing problems.
The best denial-management system is one that prevents avoidable denials.
The best administrative workflow is one that requires less administrative intervention.
And the best AI billing system may not be the one that does the most work after the claim fails.
It may be the one that helps prevent the failure from occurring.
That requires moving upstream.
Stop Asking What AI Can Automate
Start asking what information should have been available earlier.
This is an important distinction.
Healthcare technology loves the question:
“Can AI do this?”
Can AI read the note?
Can AI code the encounter?
Can AI identify a denial?
Can AI draft an appeal?
Can AI summarize the chart?
Can AI call the payer?
Fine.
But there is a more fundamental question:
Why did the system need to do that work at all?
If the information was available at the point of capture, perhaps the downstream correction would never have been necessary.
That is a different philosophy of automation.
And I think it deserves more attention.
AI Should Not Become Another Employee You Have to Manage
Healthcare has a funny relationship with technology.
We buy technology to reduce workload.
Then we create:
Another dashboard.
Another login.
Another notification.
Another inbox.
Another alert.
Another queue.
Another report.
Another thing somebody needs to check.
Congratulations.
We have successfully automated the creation of more work.
That is not the future I want.
The best technology should become almost boring.
It should quietly:
Extract.
Structure.
Validate.
Flag.
Route.
Then get out of the way.
That is particularly important in physician practices.
The physician does not need another artificial employee demanding attention.
The physician needs fewer interruptions.
What Yvonne Hao’s Comment Gets Right
At an Axios health event on September 10, Flagship Pioneering’s Yvonne Hao said the question she uses around AI is whether it connects back to the mission and whether it is being used for good.
That sounds simple.
It is actually a useful test for healthcare technology.
Ask of every new AI product:
What problem does this solve?
Then ask:
Who gets the time back?
Then:
What new work does it create?
That third question is often missing.
And it should not be.
The Physician Is Usually the Most Expensive Integration Layer in the Building
Here’s another uncomfortable observation.
When information is missing, unclear, or inconsistent, healthcare often escalates the problem to the physician.
Can you clarify the note?
Can you add the diagnosis?
Can you explain the medical necessity?
Can you sign this?
Can you answer this payer question?
Can you review this?
Can you document that?
Of course the physician does it.
The patient needs care.
The practice needs payment.
But the physician’s attention is not free.
Even when no invoice is attached to it.
Every unnecessary interruption competes with:
Patient care.
Clinical reasoning.
Family time.
Recovery.
Teaching.
Leadership.
Sleep.
We should be much more careful about consuming physician attention.
The Revenue Cycle Is a Clinical Operations Problem
Physicians sometimes think of billing as something that happens after medicine.
It doesn’t.
The revenue cycle begins much earlier.
It begins with information capture.
It begins with:
Who is the patient?
Who is the payer?
What service is being provided?
What documentation is required?
What authorization is needed?
What clinical facts support the service?
What information must move between people?
What needs to happen next?
The claim is simply the final expression of all those upstream decisions.
If the information is wrong, incomplete, late, or disconnected, the claim inherits the problem.
The 1.2-FTE Problem
Let’s make this practical.
Imagine your clinic has:
A front-desk employee spending 30 minutes a day fixing insurance information.
A nurse spending 30 minutes chasing missing documentation.
A biller spending two hours correcting claims.
A physician spending 15 minutes answering billing questions.
A practice manager spending another hour troubleshooting payer issues.
No single task looks catastrophic.
Together?
You may have created a hidden employee.
Not a productive employee.
A correction employee.
Call it the 1.2-FTE problem.
You may not have hired another person.
But your workflow is behaving as if you did.
And unlike a real employee, this invisible FTE does not show up cleanly on payroll.
It hides inside:
Inbox time.
Overtime.
A/R.
Delayed collections.
Staff frustration.
Physician interruptions.
And the occasional sentence:
“That’s just how billing works.”
“That’s Just How Billing Works” Is One of the Most Expensive Sentences in Medicine
Every industry has a sentence like this.
Healthcare has several.
“That’s just how the payer works.”
“That’s just how the EHR works.”
“That’s just how prior authorization works.”
“That’s just how claims work.”
Maybe.
But “that’s how it works” and “that’s how it has to work” are very different statements.
Physician owners should learn to ask:
Why?
Not angrily.
Not politically.
Operationally.
Why does this information need to be entered twice?
Why does the physician have to answer this?
Why isn’t this checked earlier?
Why does the biller discover it after submission?
Why can’t the system tell us before the patient leaves?
Why does this require human memory?
Those questions can expose enormous amounts of hidden work.
The AMA Data Should Make Practice Owners Pay Attention
The American Medical Association reports substantial ongoing administrative burden associated with prior authorization.
In its 2026 survey, 95% of physicians said prior authorization delays necessary care, while 79% said it sometimes leads patients to abandon treatment. The AMA also reported that physicians and staff spend an average of 13 hours per week on prior authorization work, with 40% of physicians reporting that they employ staff dedicated exclusively to those tasks.
These numbers are about prior authorization rather than the entire revenue cycle.
That distinction matters.
But the underlying pattern is familiar:
Information and administrative requirements create work far away from the original clinical decision.
That is exactly why upstream workflow deserves attention.
The AMA Also Makes a Useful Point About Private Practice
The AMA’s private-practice resources note that unpaid claims can remain unresolved for extended periods and identify coding, payer requirements, and medical-necessity requirements among contributors to denials and unpaid claims.
Again, the lesson is not:
“Fire your biller.”
Quite the opposite.
The lesson is:
Understand the work your biller is being asked to do.
Then determine which part is necessary expertise and which part is preventable rework.
Those are not the same thing.
A Simple Exercise: Follow One Claim Backward
Pick one denied claim.
Do not start with the denial code.
Start with the patient encounter.
Then walk backward.
Step 1: The Claim
What failed?
Step 2: The Documentation
Was the supporting information available?
Step 3: The Coding
Did the code accurately reflect the documentation?
Step 4: The Encounter
Was the necessary information captured?
Step 5: The Authorization
Was authorization needed?
Was it obtained?
Was the information transferred correctly?
Step 6: Eligibility
Was the patient’s coverage verified?
Step 7: Registration
Was the demographic and insurance information correct?
Then ask the most important question:
At what point could someone have prevented the problem?
That is your upstream opportunity.
Do This With 20 Claims
You do not need a six-month transformation program.
Start small.
Take 20 recent denials or delayed claims.
Put each into one of these categories:
- Eligibility
- Authorization
- Documentation
- Coding
- Medical necessity
- Demographics
- Payer rule
- Missing information
- Workflow handoff
- Unknown
Then calculate:
Which category appears most often?
Next:
Where did that information first exist?
Then:
Why wasn’t it used at the point where it could have prevented the problem?
Now you’re doing root-cause analysis.
Not denial cleanup.
The Metric I Want More Clinics to Track
Everyone tracks:
Days in A/R.
Denial rate.
Collection rate.
Clean claim rate.
Good.
Add another number:
Minutes of avoidable administrative work per encounter.
This is not a standard universal metric.
That’s precisely why I like it as a management question.
If your staff spends five unnecessary minutes per encounter and your clinic sees 1,000 encounters a month, that is approximately:
5,000 minutes.
That’s more than 83 hours.
More than two full-time workweeks.
And that’s before counting the physician’s time.
Suddenly five minutes doesn’t look so small.
Measure Rework, Not Just Output
A billing team can look incredibly productive while drowning in rework.
They can submit thousands of claims.
They can make hundreds of calls.
They can close hundreds of tasks.
And still be operating inside a broken information loop.
So measure:
First-pass acceptance
How often does information move through correctly the first time?
Rework
How many encounters require correction?
Clarification requests
How often must staff ask physicians for missing information?
Payer follow-up
How many interactions are required after submission?
Authorization delays
How often does administrative work interfere with care or reimbursement?
Time to resolution
How long does it take to turn an identified problem into a completed correction?
The goal isn’t to make staff work harder.
The goal is to make fewer things require fixing.
What OnnX Is Trying to Change
This is the problem I am working on with OnnX.
The thesis is simple:
Healthcare billing is not only a billing problem. It is an information problem.
Most revenue-cycle systems become heavily involved after information has already become fragmented.
OnnX is being built around the idea that more value can be created upstream.
At the point where clinical and operational information enters the workflow.
At the point where missing information can still be corrected.
At the point where payer requirements can still influence the process.
At the point where the practice still has a chance to prevent the downstream mess.
The goal isn’t to replace physicians.
It isn’t to eliminate billers.
It isn’t to pretend every healthcare decision can be reduced to an algorithm.
It is much simpler:
Reduce unnecessary correction loops.
Deterministic Where Possible. Human Where Necessary.
I think healthcare AI needs a little humility.
Not everything should be delegated to a model.
Some tasks are appropriate for AI.
Extract information.
Find patterns.
Summarize.
Identify missing fields.
Surface exceptions.
Other tasks require deterministic rules.
Does this required field exist?
Was authorization obtained?
Does the documentation contain the required element?
Is the payer requirement satisfied?
Then there are decisions that need human judgment.
That’s okay.
The goal is not:
AI everywhere.
The goal is:
AI where it helps, rules where rules work, humans where judgment matters.
That is a much more realistic model for healthcare.
A Good Healthcare AI Product Should Feel Almost Boring
This is probably the least exciting product pitch in Silicon Valley.
And I mean that as a compliment.
Imagine a physician using a system and thinking:
“Nothing happened.”
Exactly.
The authorization was already checked.
The information was already captured.
The missing item was already flagged.
The claim didn’t need correction.
The biller didn’t need to send the message.
The physician didn’t need to open another inbox.
Nothing happened.
That’s the product.
What About Legal and Compliance Risk?
Upstream automation doesn’t eliminate compliance obligations.
It can make them more important.
Any medical billing technology needs appropriate attention to:
- HIPAA and privacy
- Business associate relationships
- Data access
- Audit trails
- Documentation
- Coding accuracy
- Medical necessity
- Payer-specific requirements
- Human review
- Security
- Role-based permissions
- Vendor oversight
There is another important principle:
Automation should not hide accountability.
If an AI-assisted system makes a recommendation, the practice should understand what happened, what information was used, and where human review belongs.
Fast is not the same as safe.
Automated is not the same as correct.
And “AI said so” is not a compliance strategy.
The Human Connection We Risk Losing
This is where Donn Landholm’s story becomes more than a clever business metaphor.
Healthcare is full of invisible connections.
A donor in Michigan.
A patient in Houston.
A transplant team.
A spouse.
A football staff.
A laboratory.
A registry.
A coordinator.
A physician.
A nurse.
Dozens of people.
One outcome.
The system works when information moves between those people.
Revenue-cycle work is similar.
A patient.
A scheduler.
A front-desk employee.
A nurse.
A physician.
A coder.
A biller.
A payer.
Money moves only when information moves.
When the handoff fails, everyone downstream inherits the problem.
Healthcare Doesn’t Have a Technology Shortage
It has an integration problem.
Most practices already have technology.
EHR.
Practice management.
Scheduling.
Eligibility.
Clearinghouse.
Fax.
Portal.
Email.
Phone.
Spreadsheet.
Text message.
Sometimes three versions of the same spreadsheet.
Healthcare is not short on software.
It is short on coherence.
The question isn’t:
“How many systems do we have?”
It is:
“How many times does information have to become a person’s job?”
That is the metric I would pay attention to.
The Best Workflow May Be the One Nobody Notices
Nobody celebrates a claim that was never denied.
Nobody sends flowers because an authorization was obtained correctly.
Nobody writes a LinkedIn post about a demographic field that was entered correctly.
Nobody gives a standing ovation because a documentation requirement was identified before the visit.
That’s okay.
Operational excellence is often boring.
In fact, it should be.
The dramatic story happens when the system fails.
The beautiful story happens when it doesn’t.
What Physicians Should Ask Their Practice Team
Try these questions at your next staff meeting.
Where do we lose the most time?
What do you have to check manually every day?
What information do you repeatedly request from physicians?
What information gets entered more than once?
Which payer requirements cause the most rework?
Which denial category keeps coming back?
What task would disappear if we captured better information at the beginning?
And perhaps the most revealing:
What do you do every day that you think is completely unnecessary?
Listen carefully.
Your staff may already have the product roadmap.
What Healthcare Founders Should Learn From Morgan Chalup
There is also a lesson here for healthcare innovators.
Don’t begin with:
“What can our technology do?”
Begin with:
“Where is the human being experiencing unnecessary friction?”
Then ask:
What information is missing?
Who has it?
Who needs it?
When do they need it?
Why isn’t it there?
What happens when it isn’t?
What does someone do manually to compensate?
That sequence will often produce a much better product than starting with a technology capability.
The Future Is Not More Automation
That is too simple.
The future is better orchestration.
AI can help interpret information.
Rules can validate information.
Workflows can move information.
Humans can handle exceptions.
The goal is not to eliminate every human interaction.
The goal is to reserve human attention for the interactions that actually deserve it.
That’s a much more interesting definition of automation.
A 30-Day Upstream Challenge for Physician Owners
Week 1: Observe
Don’t change anything.
Watch.
Where does information enter?
Where does it disappear?
Where do people wait?
Where do they copy?
Where do they call?
Where do they ask someone else?
Week 2: Measure
Choose three problems.
Track:
- Frequency
- Time
- People involved
- Revenue impact
- Patient impact
- Physician impact
Don’t guess.
Count.
Week 3: Fix One Upstream Point
Don’t buy five tools.
Fix one thing.
Maybe eligibility.
Maybe authorization.
Maybe documentation.
Maybe demographic data.
Maybe a handoff.
Make the experiment small.
Week 4: Measure Again
Ask:
Did rework decline?
Did staff time decline?
Did physician interruptions decline?
Did claims improve?
Did patients experience less friction?
If not, change the intervention.
That’s how improvement actually works.
What Not to Do
Don’t start with:
“We need AI.”
Don’t start with:
“We need another dashboard.”
Don’t start with:
“Let’s hire more people.”
Don’t start with:
“Let’s automate everything.”
Start with:
“Where is the friction?”
Then:
“Why does it exist?”
Then:
“Can we remove the cause rather than manage the symptom?”
A Little More Humor, Because Healthcare Needs It
If your workflow requires:
Three passwords,
two browser tabs,
a payer portal,
a spreadsheet,
a sticky note,
a fax,
and one employee named Linda who “knows how this works”…
you may not have an integrated workflow.
You may have a treasure hunt.
And Linda is the map.
The Real Cost of the Treasure Hunt
The cost isn’t just labor.
It’s uncertainty.
A physician owner cannot easily predict revenue when the process depends on manual corrections.
A biller cannot work efficiently when information arrives incomplete.
A practice manager cannot optimize what cannot be measured.
And a patient doesn’t care which department caused the problem.
They simply experience the consequence.
That is why upstream design matters.
The Most Important Question for the Next Generation of Healthcare Technology
Not:
Can it replace someone?
Not:
Can it process more claims?
Not:
Can it generate a faster appeal?
Not even:
Can it use AI?
The more important question is:
Can it prevent unnecessary work from being created in the first place?
That is a much harder problem.
And potentially a much more valuable one.
Why the Donor Story Stays With Me
Morgan Chalup joined a registry in 2022.
She had no idea whether it would ever matter.
Three years passed.
Then a phone notification appeared while she was driving her daughters home.
Someone needed her.
She answered.
She donated.
Donn Landholm received the cells.
He survived.
They eventually met.
The remarkable thing is that Morgan’s most important contribution happened before she knew there was a patient.
That is what upstream thinking looks like.
You prepare before the problem becomes visible.
The Same Principle Applies to Your Practice
A claim denial is visible.
The missing information that caused it may not be.
A physician interruption is visible.
The workflow defect that caused it may not be.
A delayed payment is visible.
The documentation gap that preceded it may not be.
Staff overtime is visible.
The rework that consumed the hours may not be.
Healthcare often measures the consequence.
We should also measure the cause.
Fix the Beginning, Not Just the Ending
Donn Landholm’s story is ultimately a story about connection.
A woman in Michigan.
A patient in Houston.
A registry.
A medical team.
Two families.
One extraordinary chain of events.
The transplant was the visible miracle.
But the story began years earlier.
That is the part worth remembering.
Because healthcare has a habit of showing us the final event while hiding the chain that made it possible.
The same is true of medical billing.
The denial is visible.
The upstream failure often isn’t.
The appeal is visible.
The missing information isn’t.
The recovered revenue is visible.
The hours of rework are not.
The physician’s interruption is visible.
The broken workflow that caused it isn’t.
So perhaps the better question for physician owners isn’t:
“How do we get better at fixing denials?”
It is:
“How do we create fewer denials that need fixing?”
That is a different question.
It leads to a different product.
A different workflow.
And potentially a different way of thinking about revenue cycle management.
Get Involved
Here’s my question for physicians, clinic owners, practice administrators, and medical billers:
What is one downstream billing problem your practice keeps fixing that should have been prevented upstream?
Put the example in the comments.
Don’t give me the polished case study.
Give me the annoying one.
The ridiculous one.
The one everyone has quietly accepted as “just how healthcare works.”
Those are often the most interesting problems.
Share this article with a physician owner, practice administrator, or biller who has seen the same thing.
And if you’re building a better workflow in your own practice, raise your hand and join the conversation.
The next improvement in healthcare may not require another dramatic breakthrough.
It may require fixing something boring that should have worked the first time.
One final thought:
Donn Landholm’s story began with a woman who raised her hand before anyone knew they would need her.
Your next denial may already be taking shape before anyone sees it.
The question isn’t only how quickly your practice can fix it.
The more interesting question is whether your practice can see it coming.
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-owned clinics reduce administrative friction and improve revenue-cycle performance.
His work focuses on the intersection of clinical operations, healthcare technology, medical billing, and practice management.
His perspective is grounded in a simple question:
How can healthcare organizations spend less time correcting information and more time using it?
Connect with Dr. Cham on LinkedIn:
Continue the Conversation
Healthcare improves when clinicians, operators, founders, and patients share what is actually happening behind the workflow.
Explore additional perspectives on healthcare operations, medical technology, innovation, medical billing, and physician practice management:
Knowledge becomes useful when it changes what we do. Start with the problem you can see. Then look upstream.
Free Resource
Physicians and clinic owners can find a free revenue-cycle resource in the Featured section of my LinkedIn profile.
No signup required.
Use it as a starting point for examining where information enters your practice, where it gets lost, and where avoidable administrative work begins.
Disclaimer
This article is provided for general educational and informational purposes. It does not constitute medical, legal, coding, billing, compliance, financial, or professional advice. Healthcare requirements vary by payer, specialty, jurisdiction, contract, and clinical circumstance. Consult appropriately qualified professionals for advice regarding your specific situation.
References
1. Houston Chronicle — Donn Landholm and Morgan Chalup
Joseph Duarte’s September 10, 2026 report provides the primary account of Landholm’s CMML diagnosis, treatment, stem-cell transplant, Chalup’s donation, and the eventual connection between donor and recipient.
Read the Houston Chronicle story
2. Axios — Health Leaders on AI and Human Oversight
Axios reported on September 14, 2026, on a September 10 health event where healthcare and biotech leaders discussed AI, clinical implementation, safety, and the importance of keeping innovation connected to healthcare’s mission.
3. American Medical Association — Prior Authorization Burden
The AMA’s 2026 survey reports substantial physician and staff time devoted to prior authorization, including 13 hours per week on average and widespread reports of delays and administrative burden.
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