One teenager died from a hidden heart condition. His parents turned grief into prevention. What does that have to do with medical billing?
“Only by increasing public awareness will we help make
people realize that this is a real problem.” — Dr. John Rogers,
physician and early medical adviser to the Eric Paredes Save A Life Foundation.
A 15-year-old changed the way I think about healthcare
In 2009, Eric Paredes was 15 years old.
He was athletic.
He played football.
He wrestled.
He was a sophomore at Steele Canyon High School in San Diego
County.
And, as far as his family knew, he was healthy.
Then Eric collapsed.
He died from sudden cardiac arrest caused by an
undetected heart condition.
His mother, Rhina Paredes, was a registered nurse at
Scripps Health.
His father was Hector Paredes.
They faced something no parent should have to face.
They could not save Eric.
But they could ask a question.
A brutally simple one:
Could this have been found earlier?
That question became the beginning of the Eric Paredes
Save A Life Foundation.
The foundation began providing cardiac screenings for young
people.
Physicians volunteered.
Nurses participated.
Technicians participated.
Families showed up.
And one of those young people was Carson Wells.
Carson appeared healthy.
His previous sports physicals had not revealed the problem.
Then an EKG detected something different.
He had Wolff-Parkinson-White syndrome, a condition
involving an extra electrical pathway in the heart that can produce dangerous
arrhythmias.
Carson received treatment.
He recovered.
And eventually he chose to pursue nursing.
Read that again.
A family lost a child.
They built a system to detect risk earlier.
Another young person was found before tragedy.
That young person entered healthcare.
That is more than a touching story.
It is a lesson in how healthcare should work.
Here is the contrarian part
We often define healthcare innovation as:
new technology.
New drug.
New device.
New AI model.
New EHR.
New platform.
New algorithm.
But the Eric Paredes story suggests something different.
Innovation can simply mean finding the problem earlier.
That sounds almost too simple.
It isn't.
In fact, I think healthcare has become so obsessed with
treating downstream problems that we sometimes forget how powerful upstream
intervention can be.
The foundation did not ask:
How do we become better at responding to sudden cardiac
arrest?
It asked:
How do we identify young people who may be at risk before
sudden cardiac arrest happens?
That distinction is everything.
And it leads to a provocative question for physician owners:
What if medical billing has the same problem?
What if your billing problem isn't a billing problem?
This is where my thinking as a physician and founder of OnnX
begins.
A denial is usually treated as a billing problem.
But what if the denial is only the symptom?
Consider a simple chain.
The physician sees the patient.
Something is documented.
Something is coded.
Something is transmitted.
The payer evaluates it.
The claim is paid.
Or rejected.
If it is rejected, someone starts working the denial.
But where did the problem actually begin?
Maybe eligibility.
Maybe documentation.
Maybe coding.
Maybe authorization.
Maybe a missing modifier.
Maybe payer-specific rules.
Maybe a workflow that required someone to manually transfer
information between systems.
By the time the denial appears, the original mistake may be
several steps upstream.
And now we have a person fixing it.
That person costs money.
The delay costs money.
The rework costs money.
The physician's attention may be consumed.
The patient may experience friction.
The practice loses time.
And everyone calls it:
“a billing issue.”
I think that description is too narrow.
The medical billing industry has a strange habit
We are very good at mopping the floor.
We are not always as good at fixing the pipe.
MGMA's January 2026 survey found that denials and appeals
accounted for 48% of reported revenue-cycle leaks, followed by front-end
issues at 23%, billing and collections at 14%, coding at 13%, and charge
posting at 2%.
Those numbers should make physician owners uncomfortable.
Not because denials exist.
Denials will always exist.
The uncomfortable part is this:
What if the denial department is becoming the emergency
department of the revenue cycle?
A place where problems arrive after they have already become
expensive.
We would never design clinical medicine that way.
Imagine saying:
“We don't need preventive medicine. We'll just build a
bigger emergency department.”
Nobody would accept that.
Yet healthcare financial operations often behave similarly.
More denials → more staff → more appeals → more vendors →
more software.
Maybe we should interrupt the cycle.
The uncomfortable question
What if the goal of RCM should not be to become better at
recovering lost revenue?
What if the goal should be:
Make less revenue become lost in the first place.
That sounds obvious.
But it changes the entire strategy.
It moves us from:
recovery → prevention
back office → entire workflow
claims → encounters
denials → root causes
staffing → work redesign
automation → intelligence
That is a much bigger shift.
Eric's story gives us a framework
I see four stages.
Stage 1: Something happens.
Eric dies.
Stage 2: Someone asks why.
His parents ask whether a hidden problem could have been
detected.
Stage 3: The system changes.
A screening program emerges.
Stage 4: Someone benefits.
Carson Wells is identified before his condition becomes a
catastrophe.
Now apply the same model to RCM.
Stage 1
A claim is denied.
Stage 2
Someone asks why.
Stage 3
The practice changes the workflow.
Stage 4
Future claims avoid the same failure.
That fourth stage is where the real value lives.
Not in fixing yesterday's problem.
In preventing tomorrow's.
The biggest RCM mistake may be measuring the wrong thing
Physicians are accustomed to outcome measures.
Blood pressure.
A1C.
Readmission.
Mortality.
Length of stay.
Complication rates.
We understand that the measurement should reflect the
outcome we actually care about.
Yet financial operations sometimes become obsessed with
activity.
How many claims were submitted?
How many denials were worked?
How many calls were made?
How many appeals were filed?
How many accounts were touched?
Those are activity metrics.
They can be useful.
But activity is not the same as performance.
I would rather know:
How many problems did we prevent?
How many claims were correct the first time?
How much rework disappeared?
How quickly did the encounter become billable?
Which recurring error did we eliminate?
Those questions move us upstream.
The hidden cost nobody puts on the dashboard
Here is the metric I think healthcare leaders should start
thinking about:
Human attention consumed by preventable work.
A billing error costs more than the dollar value of the
claim.
It costs someone time.
Suppose a staff member spends 15 minutes fixing a problem.
One event means almost nothing.
Now multiply it.
20 times a week.
50 times a week.
100 times a week.
Then add:
emails.
phone calls.
payer portals.
documentation searches.
reconciliation.
follow-up.
rework.
Now the practice has a second problem.
Its workforce is being used as middleware.
People are manually connecting systems that should be able
to communicate.
That is expensive.
And it is demoralizing.
The 2026 squeeze makes this more important
MGMA reported in June 2026 that 84% of medical groups
surveyed had higher year-to-date operating costs than the same period in 2025.
Among those reporting increases, the average increase was approximately 11%.
Labor was a major contributor.
At the same time, MGMA's June revenue poll found only 47%
of practices reported higher year-to-date revenue, while 36% reported
lower revenue.
That is the squeeze.
Costs move up.
Revenue does not necessarily move with them.
And independent practices have less room for waste.
So the question becomes:
Can we afford to keep paying humans to correct problems
our systems should have prevented?
I don't think we can.
But here is another contrarian idea
The answer is not automatically AI.
I say this as the founder of an AI company.
That should tell you something.
AI is not a strategy.
AI is a capability.
If the workflow is broken, AI can make the broken workflow
faster.
If the data is bad, AI can process bad data faster.
If the process is unnecessary, AI can automate unnecessary
work.
That is not innovation.
That is acceleration.
Before asking:
“Where can we add AI?”
Ask:
“Why does this step exist?”
Then:
“What information should exist before this step?”
Then:
“Can the step disappear?”
Only then:
“Would AI make what remains better?”
This is where OnnX fits
I founded OnnX around a simple thesis:
Healthcare billing is not only a billing problem. It is
an information problem.
The claim is downstream.
The encounter is upstream.
The financial outcome is influenced long before the claim
reaches the payer.
So the opportunity is not simply to build a better claims
factory.
It is to improve the information and workflow that feed the
claims process.
That means thinking about:
clinical documentation
structured data
coding logic
payer requirements
workflow rules
claim readiness
exceptions
and feedback
as parts of one system.
The ambition is straightforward:
Catch problems earlier.
Reduce unnecessary rework.
Make financial workflows more predictable.
Give humans better information at the moment they need
it.
That is what I believe intelligent RCM should become.
But let's challenge another industry assumption
“Outsource your billing and forget about it.”
That advice is attractive.
It is also incomplete.
Outsourcing can be extremely valuable.
A good RCM partner can provide expertise and scale that a
small practice cannot build internally.
But outsourcing should not mean surrendering visibility.
If someone else manages your revenue cycle, you should still
know:
What is being lost?
Why is it being lost?
Where is it happening?
How long does recovery take?
What is preventable?
What does the vendor control?
What does your practice control?
You can outsource the work.
You cannot outsource ownership.
Another myth: “More billing staff means better billing.”
Sometimes.
But not always.
MGMA's 2026 data suggests practices are increasingly looking
toward automation and process redesign as ways to reduce costs while
making scarce staff capacity go further.
That matters because the real objective should not be:
more people touching every claim.
It should be:
fewer claims requiring human intervention.
That is different.
The future isn't fewer humans
This is where I disagree with some of the loudest AI
narratives.
I don't think the future of healthcare is:
AI replaces the biller.
I think it is:
AI removes the repetitive work that prevents the biller
from doing higher-value work.
The human becomes the exception manager.
The investigator.
The decision-maker.
The person who handles ambiguity.
The person who sees the unusual case.
The machine handles the predictable.
That is a much healthier vision of automation.
What physicians should measure instead
If I were sitting with a physician-owner tomorrow, I would
ask for ten numbers.
1. Clean claim rate
How often does the claim leave correctly the first time?
2. Denial rate
Not just the percentage.
The reason distribution.
3. First-pass resolution
How often is the claim paid without intervention?
4. Unbilled encounters
How many completed visits have not become claims?
5. Days from encounter to submission
How long does it take to move from care to billable
transaction?
6. A/R aging
Especially the portion that is becoming difficult to
recover.
7. Payment variance
Expected versus actual reimbursement.
8. Rework hours
How much human time is spent fixing recurring problems?
9. Payer-specific exceptions
Which payers create disproportionate administrative work?
10. Preventable failure rate
How many problems could have been caught before submission?
That last number may become one of the most important
metrics of the future.
A provocative new KPI: Prevention Rate
Here's a concept I would like to see more practices
experiment with.
Revenue Prevention Rate
Not:
“How much did we recover?”
But:
“How many potentially costly errors did we identify
before they became claims problems?”
It could include:
eligibility exceptions caught before the visit,
documentation gaps identified before submission,
authorization issues identified before service,
coding inconsistencies caught before claim generation,
payer-specific requirements identified before submission.
The exact formula will vary.
The philosophy is what matters.
Measure prevention.
Because what gets measured gets managed.
The 20-minute physician-owner audit
You can start this week.
No expensive software required.
Step 1: Pick 20 recent denied claims.
Not 2,000.
Twenty.
Step 2: Put them into categories.
Documentation.
Eligibility.
Authorization.
Coding.
Modifier.
Payer rule.
Timely filing.
Other.
Step 3: Trace each problem backward.
Ask:
What happened immediately before the denial?
Then:
What happened before that?
Step 4: Find repetition.
If five of 20 claims failed for the same reason, you do not
have five billing problems.
You have one workflow problem occurring five times.
Step 5: Fix the earliest failure.
Not the most visible failure.
The earliest one.
Step 6: Measure it for 30 days.
Did the problem decrease?
If yes, keep the change.
If no, investigate again.
That is continuous improvement.
What not to automate
This matters.
Do not automate a process simply because it is repetitive.
Some repetitive tasks still require judgment.
Be cautious around:
clinical interpretation
medical necessity
ambiguous documentation
compliance-sensitive decisions
patient financial communication
high-risk coding judgments
The question is not:
“Can AI do this?”
It is:
“What happens if AI gets this wrong?”
That is the better healthcare question.
Legal and compliance reality
Revenue-cycle technology operates in a highly regulated
environment.
That means physicians and founders need to think beyond
efficiency.
There are implications involving:
HIPAA
data security
coding compliance
documentation integrity
payer contracts
fraud and abuse laws
medical necessity
auditability
patient financial communications
AI should never manufacture documentation.
It should not create clinical facts that were not
documented.
It should not encourage unsupported coding.
And it should not turn “optimization” into a euphemism for
aggressive billing.
The goal is:
accurate reimbursement for appropriate care.
Not:
maximum reimbursement regardless of accuracy.
That distinction is not semantic.
It is ethical.
The ethical question nobody asks about RCM
Here is the question:
Who pays for administrative complexity?
Sometimes the payer.
Sometimes the practice.
Sometimes the physician.
Sometimes the staff.
Sometimes the patient.
Often, everyone pays a little.
That is why administrative friction is not merely an
operational annoyance.
It is an allocation problem.
Every unnecessary step consumes scarce resources.
And healthcare already has too few of them.
What the Eric Paredes story teaches healthcare founders
There are three founder lessons here.
First: Start with the human problem.
Technology comes second.
The Paredes family did not start with a technology pitch.
They started with grief.
Then a question.
Then a problem.
Then a solution.
That sequence matters.
Second: Move upstream.
The highest-value intervention may happen before the obvious
problem.
That applies to:
clinical deterioration,
readmissions,
medication errors,
prior authorization,
denials,
coding,
documentation,
and patient access.
Third: Build systems, not features.
A screening program is not just an EKG.
It requires:
people,
workflow,
follow-up,
referrals,
clinical interpretation,
communication,
and accountability.
Healthcare technology works the same way.
A billing AI model is not a healthcare system.
It is one component.
What the story teaches physician leaders
Physician leadership is often described as:
clinical excellence + business competence.
I would add a third element:
systems thinking.
The physician-owner needs to see the whole chain.
Patient.
Staff.
Documentation.
Technology.
Payer.
Payment.
Compliance.
Cash flow.
Access.
Quality.
Those things are connected.
If one becomes unstable, the others feel it.
The biggest mistake in healthcare innovation
We often optimize what is easiest to measure.
Claims.
Clicks.
Messages.
Appointments.
Transactions.
But healthcare is ultimately about outcomes.
Eric's outcome was tragic.
Carson's outcome was different.
The screening changed the trajectory.
That is the kind of story healthcare innovation should
chase.
Not:
How many tasks did we automate?
But:
What changed because we did?
A different definition of ROI
ROI should not only mean:
dollars recovered.
Consider:
hours returned to staff
claims prevented from becoming denials
faster cash
less rework
fewer manual handoffs
better visibility
less physician involvement in administrative problems
better patient financial communication
more predictable operations
The financial return matters.
But so does the human return.
The future of independent medicine may depend on this
Independent physicians are being squeezed from multiple
directions.
Operating costs are rising.
Payer complexity remains.
Regulatory requirements remain.
Staffing remains difficult.
AI is changing expectations.
MGMA's 2026 regulatory-burden report found that
administrative requirements and reimbursement pressure are contributing to
physician burnout, consolidation and threats to patient access. The survey
included more than 230 medical groups, with 60% of respondents representing
independent practices.
That is the larger story.
The question is not whether physicians dislike paperwork.
Of course they do.
The question is:
Can independent practices redesign the machinery around
care quickly enough to remain viable?
That is a much more important question.
My contrarian prediction
I believe the next generation of healthcare technology will
move away from department-specific automation.
Today:
Billing software.
EHR.
Scheduling software.
Prior authorization tools.
Credentialing platforms.
Analytics platforms.
Patient communication platforms.
Tomorrow:
connected workflow intelligence.
The technology will increasingly understand that the same
encounter produces consequences across multiple departments.
A change in clinical information can affect coding.
Coding can affect reimbursement.
Reimbursement can affect A/R.
A payer rule can affect documentation.
An authorization requirement can affect scheduling.
Scheduling can affect access.
Access affects patients.
The system needs to understand the connections.
Not merely the individual tasks.
The phrase I would like healthcare to retire
“That's just how billing works.”
I hear versions of this everywhere.
That's just how prior authorization works.
That's just how the payer portal works.
That's just how claims work.
That's just how documentation works.
That's just how the EHR works.
Maybe.
But “that's how it works” is not the same thing as:
“that's how it should work.”
Healthcare has tolerated too much friction because the
friction became familiar.
Familiarity is not efficiency.
Another phrase worth challenging
“The doctor shouldn't worry about the business.”
If the physician owns the practice, that is dangerous
advice.
The physician does not need to become an accountant.
But the physician should understand the economics of the
organization they are responsible for.
A practice that loses money cannot indefinitely deliver
excellent care.
Financial stewardship is part of clinical stewardship.
The story comes full circle
Go back to Eric Paredes.
A 15-year-old died.
His parents did not accept that the only possible response
was grief.
They asked:
What can we do differently next time?
That question created action.
Action created a system.
The system screened people.
One of those people was Carson Wells.
Carson received a diagnosis.
He received treatment.
He survived.
And he eventually chose healthcare as his profession.
That is what happens when a system learns from a tragedy.
Healthcare gets better.
Not because the past can be changed.
But because the future can be different.
And that is exactly how I think about medical billing
Every denial contains information.
Every rejected claim contains information.
Every payment variance contains information.
Every manual workaround contains information.
Every staff complaint contains information.
Every repeated exception contains information.
The mistake is treating each one as an isolated annoyance.
Maybe they are signals.
Maybe they are telling us:
The system has a weakness here.
The question is whether we listen.
The OnnX thesis in one sentence
Don't build a better system for fixing yesterday's
billing problems; build a smarter workflow for preventing tomorrow's.
That is the problem I am interested in.
Not replacing physicians.
Not eliminating every biller.
Not promising magic.
Not adding AI for the sake of AI.
Reducing avoidable friction between care and payment.
If I were rebuilding a small clinic's RCM tomorrow
I would do five things.
1. Stop looking at the denial queue first.
Look at the encounter.
2. Find the earliest failure.
Trace the problem upstream.
3. Measure rework.
Human attention is an operating expense.
4. Simplify before automating.
Don't automate unnecessary steps.
5. Use AI where judgment is supported, not replaced.
Let machines identify patterns.
Let humans handle ambiguity.
That is a much more defensible model.
Three questions for every healthcare founder
What problem are you solving before it becomes expensive?
What human attention are you giving back?
What happens when your system is wrong?
If a founder cannot answer those questions, I am not sure
the product is ready for healthcare.
Three questions for every physician-owner
Where does revenue leakage actually begin?
How much staff time is spent fixing preventable problems?
What could we eliminate rather than simply manage better?
Those questions are more valuable than another generic RCM
sales presentation.
Myth Buster
Myth: More automation automatically means better
healthcare.
False.
Bad workflow plus automation can simply produce bad outcomes
faster.
Myth: Denials are inevitable, so denial management is
enough.
False.
Some denials are unavoidable.
Repeated preventable denials are signals of a system
problem.
Myth: Outsourcing eliminates financial responsibility.
False.
You can outsource execution.
You cannot outsource accountability.
Myth: AI will replace the billing department.
Unlikely.
AI is more likely to reshape roles, especially repetitive
administrative work, than eliminate every human role. MGMA's 2026 data found
that most medical groups had not yet redesigned roles around AI, while the
changes already occurring were concentrated in practical administrative areas
such as scheduling, registration, prior authorization, billing and routine
correspondence.
Myth: The biggest RCM problem is always the biggest
denial.
Not necessarily.
The largest visible problem may be downstream from a smaller
upstream problem.
Practical 30-day challenge for physician owners
Week 1: Observe
Do not change anything.
Watch the workflow.
Follow 20 encounters.
Week 2: Measure
Categorize:
denials,
rework,
delays,
manual handoffs,
payer exceptions.
Week 3: Fix one upstream problem
Not ten.
One.
Week 4: Measure again
Did:
denials fall?
rework fall?
submission time improve?
staff hours decrease?
cash accelerate?
If nothing changed, learn why.
Then try again.
That is how real operational improvement happens.
The tools you actually need
You may already have most of them.
EHR
Practice-management system
Clearinghouse
Payer portals
Denial reports
A/R reports
Spreadsheet
Workflow map
Staff feedback
Basic analytics
And, where appropriate:
AI-assisted workflow tools
The most sophisticated technology in the world cannot
compensate for a team that does not understand the process.
The future is not autonomous healthcare
At least, that is not the future I want.
I want augmented healthcare.
Physicians augmented by better information.
Billers augmented by better workflow intelligence.
Practice managers augmented by better visibility.
Patients augmented by better financial communication.
Founders augmented by real-world clinical insight.
Technology should expand human capability.
Not make human beings disappear from the system.
The deeper lesson
The Eric Paredes story is not really about an EKG.
It is about timing.
The same test administered too late can be irrelevant.
The same information discovered too late can be expensive.
The same documentation corrected after denial creates
rework.
The same payer issue discovered after service becomes harder
to fix.
Timing is an underappreciated dimension of healthcare
technology.
Information has more value when it arrives before the
decision point.
That may be one of the most important principles in
healthcare AI.
Final Thoughts: Find the problem earlier
Eric Paredes died in 2009.
His parents, Rhina Paredes and Hector Paredes, could
not change that.
But they changed what happened afterward.
They built a prevention effort.
Physicians such as Dr. John Rogers helped turn that
effort into a clinical program.
And people such as Carson Wells became examples of
what can happen when risk is discovered before catastrophe.
The lesson is bigger than cardiac screening.
It is a way of thinking.
Find the problem earlier.
That principle belongs in medicine.
It belongs in healthcare operations.
And it belongs in medical billing.
A denial is late.
A claim rejection is late.
A/R aging is late.
A cash-flow crisis is very late.
The real opportunity is upstream.
Before the claim.
Before the denial.
Before the rework.
Before the crisis.
That is where I believe healthcare technology should
increasingly operate.
Not simply helping us recover better.
Helping us prevent better.
Get Involved — Challenge the Conventional Thinking
Here is my question for physicians and clinic owners:
What is the one recurring problem in your practice that
everyone has accepted as “just part of healthcare” — even though it probably
shouldn't be?
Tell me in the comments.
I am especially interested in problems involving:
billing
documentation
prior authorization
payer friction
staff workload
patient financial communication
EHR workflow
administrative rework
If this article challenged the way you think about RCM, share
it with another physician or practice owner.
Maybe the next useful idea will come from someone who has
been quietly fighting the same problem in a completely different specialty.
Raise your hand. Join the conversation. Challenge the
assumptions.
Find the friction before it becomes expensive.
Move upstream before the crisis.
Help build a healthcare system that gives clinicians more
time for the work only humans can do.
Continue the Conversation
I write about the intersection of medicine, healthcare
operations, medical technology, AI and entrepreneurship.
The goal is not to predict the future from a conference
stage.
It is to examine what is actually happening inside medical
practices and ask a simple question:
Can we build something better?
Explore more practical insights and founder perspectives:
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About the Author
Dr. Daniel Cham is a physician, medical consultant
and healthcare technology entrepreneur working at the intersection of medical
practice, healthcare management, medical billing and innovation.
He is the founder of OnnX, an AI-powered medical
billing SaaS platform focused on helping small and medium-sized physician
practices reduce unnecessary administrative friction and improve visibility
across the revenue cycle.
His perspective comes from approaching healthcare technology
from both sides of the equation:
the clinical side and the operational side.
His work focuses on practical questions:
How can independent physicians protect their time?
How can practices identify revenue leakage earlier?
How can AI reduce repetitive administrative work without
replacing human accountability?
And how can healthcare technology become simpler, more
useful and more aligned with the realities of everyday practice?
Explore
Dr. Cham's LinkedIn Featured section
Disclaimer
This article is intended for general educational and
informational purposes only. It does not constitute medical, legal, coding,
compliance, financial or other professional advice.
Healthcare laws, regulations, payer policies, contracts
and reimbursement requirements can change and vary by jurisdiction and
specialty.
Physicians, healthcare organizations and practice owners
should consult appropriately qualified professionals regarding decisions
specific to their circumstances.
References to AI, OnnX or other technologies describe
concepts and perspectives and do not guarantee particular clinical, financial,
operational or regulatory outcomes.
References
1. Scripps Health — A Local Teen's Death Results in
Program Designed to Save Lives.
The foundational account of Eric Paredes' death, his parents' response, and the
early work of the Eric Paredes Save A Life Foundation with Scripps physician John
Rogers, MD.
2. MGMA — Detecting and Fixing Leaks Across the Revenue
Cycle.
MGMA's 2026 analysis provides current data on where medical practices report
revenue-cycle leakage, including the finding that denials and appeals
represented 48% of reported leaks in its January poll.
3. MGMA — 2026 Operating Costs and Practice Economics.
Current MGMA data illustrates the financial pressure facing medical practices,
including rising operating costs and the growing role of automation and
workflow redesign.
Read
MGMA's 2026 practice-cost analysis
The Last Question
Eric's parents asked a question after the worst thing
imaginable happened:
Could we find the problem earlier next time?
That question helped create a movement.
Now ask yourself the same question about your practice.
Could your practice find its next denial before the claim
is submitted?
Could you identify the next workflow failure before it
consumes another 100 staff hours?
Could you see the next revenue leak before it becomes an
A/R problem?
Because perhaps the future of healthcare is not about
getting better at cleaning up yesterday's mistakes.
Perhaps it is about becoming much better at seeing
tomorrow's problems today.
Find the signal.
Move upstream.
Prevent what can be prevented.
That is not just better billing.
That is better healthcare infrastructure.
#Healthcare #MedicalBilling #RevenueCycleManagement #RCM
#PhysicianPractice #PrivatePractice #HealthcareAI #MedicalPracticeManagement
#PhysicianEntrepreneur #HealthTech #HealthcareInnovation #IndependentPractice
#HealthcareLeadership #ClinicalOperations #AIinHealthcare #DigitalHealth
#PhysicianLeadership #PatientCare #HealthcareTechnology #PracticeManagement
Knowledge drives progress. Start your journey here.
If this perspective resonates, repost it so another
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The conversation starts when we stop accepting “that's
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