What a mother’s recovery story—and a courtroom tragedy—can teach physicians about diagnosis, data, AI, and the hidden problems inside medical billing
“When you don't have a clear definition, and you have a
very rare disorder, and there's a lot of stigma in society for folks disclosing
some of these symptoms…” — Dr.
Katrina Furey, psychiatrist specializing in women's mental health
There is a sentence in healthcare that sounds almost too
obvious to be controversial:
We need more data.
More data.
More dashboards.
More AI.
More analytics.
More interoperability.
More alerts.
More automation.
More information.
But what if we have been asking the wrong question?
What if healthcare doesn't primarily suffer from an
information shortage?
What if we suffer from an information-connection problem?
Consider the story of Haley Ashcom.
After giving birth to her first son, Kane, in 2020,
Ashcom began experiencing insomnia and anxiety. She was diagnosed with
postpartum depression. But her symptoms progressed. She told CBS News that
intrusive thoughts eventually began “turning into voices.” She ultimately
sought psychiatric hospitalization and says she was diagnosed with postpartum
psychosis. After treatment, she recovered and later had three more children
without experiencing postpartum psychosis again.
Her story is remarkable for a simple reason:
The information changed.
And eventually, the interpretation changed with it.
Now put that story next to the name Lindsay Clancy.
Clancy, a former labor-and-delivery nurse from Duxbury,
Massachusetts, is currently on trial over the 2023 deaths of her three
children, Cora, Dawson, and Callan Clancy. Her defense argues that
postpartum psychosis rendered her not criminally responsible. Prosecutors
dispute that interpretation. As of August 21, 2026, the defense has rested its
case.
These are not the same story.
They should not be treated as the same story.
Haley Ashcom's story is a story of illness, recognition,
treatment, and recovery.
The Lindsay Clancy case is an unresolved criminal proceeding
involving profoundly tragic deaths and competing medical and legal
interpretations.
But there is a question connecting them.
And it is a question I think every physician, healthcare
executive, and healthcare technology founder should be asking:
What happens when the signals are there—but nobody
connects them soon enough?
That question goes far beyond postpartum mental health.
It reaches directly into the physician practice.
And eventually, into the billing office.
The Uncomfortable Truth
Here's my contrarian take:
Your practice may not have a billing problem.
It may have a signal-recognition problem.
The denial is just where the problem finally becomes
visible.
That is an uncomfortable distinction.
Because if you call it a billing problem, you can hire
another biller.
Buy another software platform.
Add another dashboard.
Create another work queue.
Outsource another function.
And congratulate yourself on being “proactive.”
Meanwhile, the same problem keeps coming back.
Like a sequel nobody asked for.
Start With Haley Ashcom
Ashcom's story deserves to be treated as a human story
first.
Not as a metaphor.
Not as a marketing prop.
Not as evidence that some piece of technology would have
magically solved everything.
She experienced a serious medical condition.
She sought help.
Her symptoms evolved.
Her clinical understanding evolved.
Treatment followed.
And she recovered.
That alone is worth telling.
CBS News reports that postpartum psychosis is rare,
affecting approximately 1 to 2 women per 1,000 after delivery, but can
be dangerous and requires urgent treatment.
ACOG similarly describes postpartum psychosis as a very
rare and serious condition and recommends immediate psychiatric help.
But there is a subtle lesson here.
The important thing wasn't simply that information existed.
The important thing was that the meaning of the
information changed as the story developed.
Insomnia.
Anxiety.
Intrusive thoughts.
Voices.
Escalation.
Hospitalization.
Diagnosis.
Treatment.
Recovery.
Healthcare is rarely one data point.
It is a sequence.
A pattern.
A story.
Now Think About Your Clinic
A patient arrives.
The front desk captures insurance information.
The eligibility response comes back.
The patient sees the physician.
The physician documents the encounter.
A diagnosis is recorded.
A procedure is performed.
A code is selected.
An authorization may or may not exist.
A claim is generated.
The claim is submitted.
Then:
DENIED.
The billing department gets the unpleasant little
notification.
Someone opens the account.
Someone investigates.
Someone corrects it.
Someone resubmits it.
Someone waits.
Someone follows up.
Eventually, someone gets paid.
And everyone says:
“Good. We fixed the denial.”
Did you?
Or did you just repair the symptom?
The Denial Is Talking to You
This is the part I wish more healthcare leaders would take
seriously.
A denial is not merely an administrative nuisance.
It is information.
It is telling you something.
Maybe:
The documentation didn't support the claim.
Maybe:
The authorization wasn't obtained.
Maybe:
Eligibility wasn't verified correctly.
Maybe:
The payer's rules changed.
Maybe:
The coding didn't match the service.
Maybe:
The information existed in the chart but wasn't available
to the billing workflow.
Maybe:
Your staff has been compensating for a broken process
manually for six months.
The denial is saying:
“Hey. Something happened upstream.”
And we're often responding:
“Thanks. We'll work the queue.”
That is the healthcare equivalent of putting a Band-Aid on
the dashboard warning light.
The Question We Should Ask Instead
Don't ask:
“How do we work denials faster?”
Ask:
“Why did this claim become a denial?”
Then go one level deeper.
Why wasn't the problem detected before submission?
Then deeper:
Where did the necessary information exist?
Then:
Who had it?
Then:
When did they have it?
Then:
Why didn't the system connect it to the decision?
That is where things get interesting.
The Hidden Architecture of a Medical Practice
Most physician owners don't think of their practice as a
data architecture.
They think:
Patients.
Staff.
Phones.
EHR.
Billing.
Insurance.
Payroll.
Maybe a little coffee.
But underneath all of that is an information system.
Information moves.
Or doesn't.
That distinction is worth money.
A patient's insurance information moves from registration to
eligibility.
Clinical information moves from physician to documentation.
Documentation moves into coding.
Coding moves into claims.
Claims move to payers.
Payer responses move back into billing.
Denial information should move backward into the workflow.
But often it doesn't.
That last step is the problem.
The system forgets.
The biller learns.
The manager learns.
The physician learns.
The practice's spreadsheet learns.
But the system forgets.
So the same problem happens again.
Most healthcare organizations don't have a learning
problem. They have a memory problem.
We learn something.
Then we lose it.
A biller discovers that a payer requires something unusual.
She remembers.
Then she leaves.
Knowledge leaves with her.
A staff member discovers that a certain workflow causes
recurring denials.
He creates a spreadsheet.
Then the spreadsheet becomes obsolete.
A physician learns that documentation for a particular
service needs additional detail.
She remembers.
Then six months later, the process changes.
The organization keeps relearning the same lesson.
That's expensive.
The Human Brain Is Doing the Work Your Software Should Be
Doing
Your best employees probably have a mental model of your
practice.
They know:
“That payer always does this.”
“That provider needs this.”
“Check this before submitting.”
“Don't forget that authorization.”
“Look at the old note.”
“This one is unusual.”
Those people are valuable.
But here's the problem.
You have turned your employees into biological middleware.
They are the integration layer.
They move information between systems.
They remember exceptions.
They translate one workflow into another.
They catch things the software doesn't.
And then we wonder why they're exhausted.
This Is Where AI Actually Gets Interesting
Not because AI can “replace the biller.”
That pitch is getting old.
And frankly, it's not particularly intelligent.
The better question is:
Can AI help the practice remember what it has already
learned?
Can it recognize patterns across thousands of claims?
Can it identify recurring problems?
Can it compare current documentation against known
requirements?
Can it flag missing information before submission?
Can it identify that five apparently different denials are
actually the same problem?
Can it surface an exception for human review?
Can it explain why something was flagged?
That is much more interesting.
AI Should Not Be the Doctor
And it shouldn't be the biller either.
At least not in the simplistic sense.
Healthcare AI should not be built around:
“Let the machine decide.”
It should be built around:
“Let the machine notice.”
Notice patterns.
Notice inconsistencies.
Notice missing information.
Notice changes.
Notice exceptions.
Then:
Let humans decide.
That is a much healthier relationship between AI and
healthcare.
The Clinical Lesson
This is exactly why Haley Ashcom's story is so powerful.
Her experience demonstrates why context matters.
A symptom is not a diagnosis.
A data point is not a conclusion.
A pattern can change the interpretation.
ACOG's guidance emphasizes screening and diagnosis across
pregnancy and postpartum care and specifically calls for immediate medical
attention when postpartum psychosis is present.
The clinical lesson is simple:
Don't just collect the signal. Understand the signal in
context.
That principle belongs everywhere in healthcare.
And Then There's Lindsay Clancy
The Clancy case makes the question much more uncomfortable.
The current trial has featured conflicting expert testimony
about her mental state, including testimony from defense expert Dr. Phillip
Resnick and prosecution expert Dr. Avram Mack. Family members and
clinicians have also offered different perspectives on her mental-health
history and behavior.
This is precisely why healthcare professionals should be
careful.
We don't know the final legal answer.
We shouldn't pretend we do.
We shouldn't use an active criminal case to make a
simplistic point about mental illness.
And we certainly shouldn't say:
“If only someone had used AI…”
That would be irresponsible.
The appropriate lesson is much more modest:
Complex clinical situations require context,
coordination, careful interpretation, and humility about what we do and do not
know.
That is true in court.
It is true in the clinic.
It is true in the billing office.
Healthcare's Favorite Mistake
We confuse documentation with understanding.
If something is documented, we assume someone understands
it.
Not necessarily.
The EHR may contain the information.
The payer portal may contain the information.
The authorization system may contain the information.
The billing system may contain the information.
The physician may know the information.
The patient may have told someone the information.
And somehow...
Nobody connects it.
That is not a data problem.
That's an architecture problem.
Your EHR May Be Full of Information and Still Be Blind
This sounds contradictory.
It isn't.
Imagine a warehouse filled with boxes.
Everything you need is inside.
But the labels are inconsistent.
Some boxes are in another warehouse.
Some are locked.
Some are written in shorthand.
Some are outdated.
Some are duplicated.
Some are only accessible to certain people.
And nobody has a map.
Technically:
You have everything.
Operationally:
You have nothing when you need it.
That's healthcare data today in miniature.
Why Physician-Owned Practices Feel This More
Large health systems can throw people at complexity.
Small and medium-sized practices can't.
A hospital might have:
coding specialists
revenue-cycle analysts
IT teams
data engineers
compliance departments
authorization teams
informatics specialists
practice administrators
A ten-provider clinic?
Maybe it has:
one office manager
two billers
a front desk
an outsourced service
and somebody named Linda who somehow knows everything.
Every practice has a Linda.
Don't make Linda your data architecture.
The Real Cost of Administrative Friction
People often ask:
“How much revenue are we losing?”
That's important.
But it is only half the question.
Ask:
How much human attention are we wasting?
Every unnecessary denial creates work.
Every missing authorization creates work.
Every eligibility problem creates work.
Every documentation correction creates work.
Every payer phone call creates work.
And every repeated problem creates work again.
The hidden cost is cognitive.
Your staff spends time remembering what your systems should
remember.
Your physicians spend time navigating what your systems
should connect.
Your practice manager spends time explaining what your
dashboards should reveal.
That's expensive.
The Physician Tax Nobody Puts on the Invoice
There is another cost.
Physician attention.
When the practice is poorly designed, administrative
problems eventually climb upstream.
A staff member asks the physician for clarification.
The physician answers.
Another question appears.
Another message.
Another chart.
Another documentation request.
Another payer issue.
Five minutes here.
Ten minutes there.
By Friday, the physician has spent hours solving problems
that had nothing to do with practicing medicine.
We don't call that a revenue-cycle expense.
Maybe we should.
The New Metric: Cognitive Waste
Here's a metric I'd like healthcare leaders to experiment
with:
Cognitive Waste
How many physician or staff minutes are spent resolving
problems that could have been prevented with better information, workflow, or
automation?
You could calculate it.
Track:
manual touches
repeated corrections
avoidable phone calls
avoidable chart reviews
duplicate data entry
repeated payer research
physician clarification requests
staff escalations
Then ask:
What percentage of this work actually needed a human
brain?
That's the interesting number.
What OnnX Is Trying to Change
This is the problem I am working on with OnnX.
The goal isn't simply to process claims.
It is to create a more intelligent revenue-cycle operating
layer for physician-owned practices.
The philosophy is straightforward:
Capture the right information.
Connect it.
Check it early.
Learn from what goes wrong.
Prevent the same problem from happening again.
That's very different from:
“Let's work the denial faster.”
Denial Management Is Not the Enemy
To be fair, denial management isn't bad.
You need it.
Claims will be denied.
Payers will make mistakes.
Rules will change.
Patients will change insurance.
Humans will make errors.
No serious operator believes in a zero-denial universe.
The problem is when denial management becomes the business
model.
If your organization is getting better at working the same
denial every month, congratulations.
You've become very efficient at being inefficient.
That's not transformation.
Denial Prevention Is a Different Game
Denial prevention asks:
Can we identify the failure before submission?
That means looking at:
Eligibility
Authorization
Documentation
Coding
Medical necessity
Provider information
Payer-specific requirements
Patient demographics
Referral requirements
Historical denial patterns
And then asking:
Does everything make sense together?
That's where contextual intelligence becomes useful.
Three Questions Every Clinic Owner Should Ask Monday
Morning
Don't start with a six-month digital transformation project.
Start small.
Ask your team:
1. What problem do we fix every single week?
Write down the answer.
2. What do you check manually because you don't trust the
system?
This answer may be even more valuable.
3. What do we discover only after the claim is denied?
That is where the money is hiding.
The 30-Day Challenge
Here's a practical experiment.
Week One: Find the Pain
Take your last 100 denials.
Categorize them.
Don't make 37 categories.
Start with five or ten.
Look for repetition.
Week Two: Trace the Crime Scene
For your top three denial categories, work backward.
Where did the problem originate?
Registration?
Eligibility?
Scheduling?
Clinical documentation?
Coding?
Authorization?
Payer configuration?
Nobody gets blamed.
You're mapping the crime scene.
Week Three: Move the Check Upstream
Take one recurring problem.
Create a check before the claim goes out.
Do it manually if you have to.
The goal is proving that prevention is possible.
Week Four: Automate the Boring Part
Once you understand the rule, automate what is predictable.
Keep humans involved where judgment matters.
Measure the result.
Then repeat.
What to Measure
Forget vanity dashboards.
Measure things that tell you whether the practice is getting
smarter.
Preventable denial rate
Recurring denial rate
First-pass acceptance
Manual touches per claim
Average correction time
Days in A/R
Staff minutes per claim
Authorization failure rate
Eligibility failure rate
Documentation-related denials
Revenue leakage
And perhaps the most interesting:
How many times did we solve the same problem twice?
If the answer is “a lot,” your system isn't learning.
The Myth of the Perfect Dashboard
A dashboard can tell you:
“Your denial rate is 11.8%.”
Okay.
Now what?
The better system says:
“These three patterns account for 74% of your preventable
denials.”
Even better:
“This one workflow change could prevent two of them
before submission.”
That's the difference between reporting and intelligence.
The Myth of More Data
More data is not automatically better.
Sometimes more data is just more noise.
Physicians don't need another 47 notifications.
Billing staff don't need another 19 queues.
Clinic owners don't need another dashboard with seventeen
shades of red.
They need:
the right information
at the right time
in the right context
with the right next action.
That's it.
The Myth of “AI Will Fix Healthcare”
No.
AI will not fix healthcare.
Neither will blockchain.
Neither will interoperability alone.
Neither will another EHR.
Neither will another billing vendor.
Technology doesn't fix poorly designed processes simply
because it is newer.
AI can make a good process better.
It can also make a bad process faster.
That's why the first question should never be:
“Where can we use AI?”
Ask:
“Where are we repeatedly losing time, information, or
money—and why?”
Then decide whether AI belongs there.
The Legal and Ethical Line
This matters enormously.
Healthcare AI must not become a machine for maximizing
reimbursement at any cost.
Accurate coding is not aggressive coding.
Complete documentation is not manufactured documentation.
Revenue optimization is not justification for unsupported
claims.
The standard should remain:
accurate clinical care
→ accurate documentation
→ accurate coding
→ accurate claim
→ appropriate reimbursement
Technology should strengthen that chain.
Not corrupt it.
And every automated recommendation should have appropriate
governance, review, auditability, and human accountability.
The Most Dangerous AI Is the AI Nobody Questions
Here's another contrarian thought.
We spend a lot of time worrying about hallucinations.
We should.
But healthcare has another AI problem:
false confidence.
A system produces a recommendation.
It looks polished.
It has a confidence score.
Everyone assumes the machine knows.
But nobody asks:
What information did it not see?
That's the dangerous question.
Because context can change everything.
The Future Isn't Human vs. AI
That's yesterday's debate.
The real future is:
Human judgment + machine pattern recognition + better
information architecture.
Let machines search.
Let machines compare.
Let machines detect.
Let machines prioritize.
Let machines remember.
Let humans interpret.
Let humans decide.
Let humans remain accountable.
That's a partnership worth building.
The Bigger Opportunity
I don't think the next great healthcare company will
necessarily be the company with the most impressive AI model.
It may be the company that understands where information
gets lost between one healthcare action and the next.
That's a much harder problem.
And probably a much more valuable one.
Because healthcare is full of gaps.
Between:
patient and provider
provider and EHR
EHR and billing
billing and payer
payer and practice
practice and patient
And every gap creates friction.
The Hidden Business Model of Healthcare
Here's the uncomfortable part.
A lot of healthcare technology is built around managing the
consequences of fragmentation.
One company handles this.
Another handles that.
Another cleans up the first company's mistakes.
Another analyzes the cleanup.
Another sells you a dashboard showing the cleanup.
And somewhere at the bottom of the stack:
a human fixes everything.
We call this an ecosystem.
Sometimes it's just expensive plumbing.
What If We Designed From the Other Direction?
Instead of:
How do we manage the complexity?
Ask:
How do we remove the complexity?
Instead of:
How do we work more denials?
Ask:
How do we create fewer preventable denials?
Instead of:
How do we give staff more tools?
Ask:
How do we give staff fewer things to do?
Instead of:
How do we make AI smarter?
Ask:
How do we make the workflow smarter?
That's the contrarian shift.
The Human Story Comes Back
This is why I keep coming back to Haley Ashcom.
Her story isn't really about technology.
It's about something much more basic.
Pay attention to the story.
Listen when something changes.
Don't isolate one symptom from everything around it.
Don't assume yesterday's explanation still fits today's
information.
And don't confuse the existence of data with understanding.
Those are clinical lessons.
But they're also leadership lessons.
And they're also technology lessons.
What Physicians Should Take Away
If you're a physician-owner, I would not ask you to become a
revenue-cycle expert.
I would ask you to become curious.
Walk into your billing operation.
Ask:
What are we repeatedly fixing?
Ask:
What information are you missing?
Ask:
What do you know that the system doesn't know?
Ask:
What do we discover too late?
Then listen.
Don't defend the system.
Don't explain why the vendor can't do it.
Just listen.
You may discover that the biggest problem in your revenue
cycle is hiding in plain sight.
What Founders Should Take Away
If you're building healthcare technology, resist the
temptation to sell features.
Sell fewer problems.
That's harder.
But better.
Don't say:
“Our platform has AI-powered analytics.”
Say:
“We found a recurring problem that costs clinics money
and staff time. Here's how we detect it earlier.”
Don't sell intelligence.
Demonstrate it.
What Healthcare Leaders Should Take Away
The future healthcare organization won't necessarily be the
one with the most technology.
It will be the one that learns fastest from what happens
inside the organization.
Every denial becomes a lesson.
Every exception becomes a pattern.
Every staff workaround becomes product feedback.
Every patient complaint becomes information.
Every operational failure becomes an opportunity to redesign
the system.
That is a learning organization.
And that is where healthcare should be heading.
FAQ
Is this article suggesting that Haley Ashcom's experience
could have been solved by technology?
No.
That would be an irresponsible conclusion.
Her story is fundamentally a human and clinical story about
postpartum psychosis, recognition, treatment, and recovery. Technology may
support healthcare, but it does not replace clinical judgment.
Why include Lindsay Clancy's case?
Because the current trial has renewed national attention to
postpartum psychosis. But it is an active legal proceeding, and the article
deliberately avoids making conclusions about Clancy's criminal responsibility.
Is postpartum psychosis the same as postpartum
depression?
No. They are distinct conditions. ACOG describes postpartum
psychosis as very rare and serious and says it requires immediate psychiatric
help.
How common is postpartum psychosis?
CBS News reports approximately 1–2 cases per 1,000 women
after delivery.
What does this have to do with medical billing?
The connection is not that psychiatric illness and billing
are equivalent.
The connection is that both involve signals, context,
interpretation, timing, and action.
Is denial management unnecessary?
No.
Denial management remains necessary.
The contrarian point is that organizations should not
confuse being good at recovering from preventable failures with being good at
preventing those failures.
Should clinics replace billers with AI?
No.
The better objective is to eliminate repetitive work and
allow experienced people to spend more time on exceptions, judgment,
communication, and complex cases.
What should clinics automate first?
Start with repetitive, measurable, rule-based work where
errors are frequent and preventable.
What is the biggest AI mistake healthcare organizations
make?
Using AI before fixing the workflow.
Garbage in, garbage out is still true—even when the
garbage is processed by an expensive model.
Three Expert Perspectives
Dr. Uruj Kamal Haider — Perinatal Psychiatry
Haider's explanation of postpartum psychosis underscores how
profoundly psychosis can affect perception and information processing.
Lesson for healthcare leaders: Context changes
interpretation.
Dr. Catherine Birndorf — Reproductive Psychiatry
Birndorf has highlighted concern about postpartum psychosis
being underdiagnosed.
Lesson for healthcare leaders: A problem can be
serious, visible, and still systematically under-recognized.
Dr. Phillip Resnick — Forensic Psychiatry
Resnick testified for the defense in the Clancy trial and
offered the opinion that Clancy was psychotic at the time of the killings. That
is an expert opinion presented in an adversarial proceeding, not an established
fact.
Lesson for healthcare leaders: Complex clinical
situations can generate radically different interpretations of the same
underlying information.
That should make us humble about automated conclusions.
Recent News
The timing of this conversation is important.
On August 21, 2026, CBS News published Haley Ashcom's
account of postpartum psychosis as the Lindsay Clancy trial continues to draw
attention to the condition.
The same day, AP reported that Clancy's defense had rested
after presenting testimony centered on her mental state and the defense's
argument that postpartum psychosis affected her criminal responsibility.
ACOG's guidance reinforces that postpartum psychosis is a
medical emergency requiring immediate attention.
The news cycle gives us the hook.
But the lesson is evergreen:
Healthcare needs to get better at connecting signals
before the consequences become irreversible.
Three References
1. CBS News — Haley Ashcom's postpartum psychosis
experience
A current human-interest account of Ashcom's symptoms, diagnosis, treatment,
recovery, and decision to speak publicly about postpartum psychosis.
Read
the CBS News report
2. Associated Press — Lindsay Clancy trial
Current reporting on the defense resting its case and the competing medical
interpretations presented during the trial.
Read
the AP report
3. American College of Obstetricians and Gynecologists —
Perinatal Mental Health
Clinical guidance covering screening, diagnosis, and the need for immediate
medical attention in postpartum psychosis.
Read
ACOG guidance
Final Thoughts
Maybe the biggest healthcare innovation isn't another app.
Maybe it's a healthcare system that actually remembers
what it has learned.
A patient tells you something.
A nurse notices something.
A physician documents something.
A family member raises a concern.
A biller sees a recurring denial.
A staff member creates a workaround.
A payer sends a signal.
The information is already there.
The question is:
Who connects it?
That is the opportunity.
And perhaps the most provocative question of all is this:
How much of healthcare's waste exists not because we lack
information, but because nobody connected the information soon enough?
Think about that the next time your billing department says:
“We have another denial.”
Maybe the denial isn't the problem.
Maybe it's finally telling you where the problem began.
Get Involved
What is one recurring problem in your practice that
everyone has learned to work around—but nobody has actually fixed?
Tell me in the comments.
Share this article with a physician or clinic owner who
needs to see this conversation differently.
And if you believe physician-owned practices deserve
technology designed around less friction, better information, and fewer
unnecessary administrative handoffs, join the conversation.
Raise your hand.
Challenge the conventional workflow.
Find the signal.
Fix the source.
Because healthcare doesn't need another system that makes
the old system slightly faster.
It needs systems that make the old problems less
necessary.
About the Author
Dr. Daniel Cham is a physician, healthcare technology
consultant, and founder of OnnX, an AI-powered medical billing SaaS
focused on reducing administrative friction for small and medium-sized
physician-owned practices.
His work sits at the intersection of clinical medicine,
healthcare operations, medical billing, artificial intelligence, and healthcare
innovation.
He is particularly interested in a simple question:
How can technology help physicians spend less time
fighting the system and more time practicing medicine?
Connect with Dr. Cham on LinkedIn:
linkedin.com/in/daniel-cham-md-669036285
Continue the Conversation
Healthcare is changing quickly.
The most useful ideas often appear where medicine,
technology, operations, and human experience collide.
For additional perspectives, practical strategies, and
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Disclaimer
This article is intended for general educational and
informational purposes only. It does not constitute medical, legal,
compliance, coding, reimbursement, financial, or other professional advice.
Clinical decisions should be made by qualified healthcare
professionals based on the individual patient and applicable clinical guidance.
Questions involving billing, coding, reimbursement,
contracts, privacy, compliance, or legal obligations should be reviewed with
appropriately qualified professionals and the applicable requirements.
The discussion of the Lindsay Clancy criminal proceedings
is provided for healthcare and educational context only and should not be
interpreted as a determination of criminal responsibility, medical causation,
or legal fact.
One More Thing
Don't ask whether your practice has enough data.
Ask whether your practice can see what the data is trying
to tell you.
Don't ask how quickly you can work the next denial.
Ask why you keep receiving the same denial.
Don't ask where you can add AI.
Ask where better information could prevent unnecessary human
work.
That's where the interesting healthcare problems are.
And that's where the next generation of healthcare
innovation should begin.
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#RevenueCycleManagement #RCM #DenialPrevention #HealthcareOperations
#PhysicianLeadership #PhysicianOwnedPractices #HealthcareTechnology #HealthTech
#MedicalTechnology #ClinicalData #HealthcareData #AIinHealthcare
#HealthcareManagement #PracticeManagement #PhysicianEntrepreneur #DigitalHealth
#HealthcareTransformation #RevenueCycle #MedicalPractice #HealthcareLeadership
#OnnX
