A devastating medication error in Nashville exposes a bigger healthcare problem: when bad information travels too far, patients, physicians, and practices pay the price.
“We identified the cause and have implemented corrective
safeguards.” — Dr.
Shubhada Jagasia, President and CEO, Ascension Saint Thomas Hospital
Midtown
The Problem With Calling Healthcare Errors “Human Error”
Glenda Dorton did not go to the hospital looking for a
miracle.
She went looking for a knee replacement.
On August 14, 2026, the 72-year-old avid gardener from
Centerville, Tennessee, arrived at Ascension Saint Thomas Hospital Midtown
in Nashville for what was supposed to be a routine procedure.
Her husband, Marvin Dorton, had been married to her
for more than 50 years.
Her family expected a difficult recovery.
They did not expect their lives to change.
According to her family, Glenda woke after the procedure
with a burning sensation and could not feel or move her lower body.
Her daughter-in-law, Kristina Dorton, later said the
family was told Glenda had T6 paralysis.
The knee replacement itself had reportedly gone well.
The rest of the story did not.
Glenda's family says she received the wrong medication
during the procedure. The hospital initially described an unidentified event
affecting four patients. It has since acknowledged a pharmacy error
involving four joint-replacement patients and said it implemented
additional safeguards. The Tennessee Bureau of Investigation is investigating.
The other three patients have not been publicly identified.
And this is where the story gets bigger than Glenda.
Because Kristina Dorton did not respond by saying:
“Find someone to blame.”
She said:
“We’re not looking to ruin somebody’s life. They need to
figure it out, so it never happens.”
That sentence contains a lesson for every healthcare leader.
The goal of patient safety is not to find the guilty
person.
The goal is to build a system in which the next person does
not have to become the next Glenda Dorton.
And that brings me to something that may sound strange at
first.
What does Glenda Dorton's story have to do with medical
billing?
Quite a lot.
Here Is My Contrarian Take
Healthcare has spent decades trying to make people work
harder inside broken workflows.
We call it:
Quality improvement.
Revenue-cycle optimization.
Workflow enhancement.
Digital transformation.
Artificial intelligence.
Automation.
Interoperability.
Sometimes it is just a very expensive way of saying:
“We built another screen.”
I think we are asking the wrong question.
The question should not be:
How can we make the workflow faster?
It should be:
How can we make the workflow harder to get wrong?
That is a very different problem.
And it is the problem that interests me as a physician and
founder of OnnX.
Four Patients Should Change the Question
The Nashville incident reportedly involved four patients
undergoing joint-replacement procedures.
That number matters.
One error can be a terrible individual mistake.
Four patients affected by the same event suggests something
larger.
It suggests that healthcare leaders should look beyond the
person who touched the medication.
They should examine the system that allowed the same
problem to reach multiple patients.
That does not mean blaming the pharmacy.
It does not mean blaming the physician.
It does not mean blaming the anesthesiologist.
In fact, Glenda's family has specifically emphasized that
they do not want individual healthcare workers blamed.
It means asking a better question:
Where was the last opportunity to catch the error?
That question is incredibly important.
Because the same question applies to medical billing.
The Billing Error Nobody Sees
Imagine this.
A physician sees a patient.
The visit goes well.
The physician documents the encounter.
The patient goes home.
Everyone thinks the job is finished.
It isn't.
Now the documentation becomes data.
The data becomes codes.
The codes become a claim.
The claim enters a clearinghouse.
The payer interprets the claim.
And eventually, someone discovers something is wrong.
Maybe the diagnosis doesn't support the procedure.
Maybe a modifier is missing.
Maybe the documentation doesn't clearly support the billed
service.
Maybe payer policy has changed.
Maybe the authorization doesn't match.
Maybe eligibility information was wrong.
Maybe the claim contains a subtle inconsistency nobody
noticed.
The claim is denied.
Then somebody starts digging.
The physician gets an email.
The biller opens the chart.
The coder reviews the note.
The office manager gets involved.
Someone calls the payer.
Someone resubmits.
Someone waits.
And someone says:
“Why didn't we catch this earlier?”
Exactly.
Why didn't we?
Healthcare Has a Timing Problem
This is the part of healthcare technology that doesn't get
enough attention.
An error becomes more expensive the longer it travels.
At the beginning, an error may take seconds to fix.
Later, it may take minutes.
After submission, it may take hours.
After denial, it may take days.
After an appeal, potentially much longer.
The same principle applies clinically.
A medication mistake caught before administration is a near
miss.
A mistake caught immediately after administration may still
allow rapid intervention.
A mistake discovered after harm occurs becomes something
entirely different.
Timing matters.
The earlier the intervention, the cheaper and safer the
correction.
That should be the foundation of modern healthcare
technology.
The Industry's Favorite Mistake
We often build systems that are excellent at documenting
what happened.
But we are less good at preventing what is about to happen.
Our systems are retrospective.
They tell us:
Here is the denial.
Here is the missed authorization.
Here is the coding error.
Here is the medication event.
Here is the complaint.
Here is the unpaid claim.
Here is the problem.
Thanks.
Could you have told us five minutes earlier?
That is where predictive and validation technology becomes
interesting.
Not because AI is fashionable.
Because timing is everything.
The OnnX Thesis
This is why I founded OnnX.
My belief is simple:
Medical billing is not primarily a billing problem. It is
a data-quality problem.
The biller is often blamed for a problem that started
somewhere else.
The coder is asked to fix documentation that was incomplete
upstream.
The billing department is asked to solve payer problems
created by inconsistent information.
The physician is pulled back into the chart.
And everyone wonders why revenue-cycle management is so
expensive.
The answer is often hiding in plain sight.
The claim is arriving at the billing department carrying
problems that should have been detected earlier.
Stop Calling It Revenue-Cycle Management
Maybe we need a different phrase.
Instead of:
Revenue-cycle management
I would argue for:
Revenue-cycle reliability.
Because management sounds like:
Process the claims.
Follow up on denials.
Work the A/R.
Send appeals.
Reliability asks:
Why did the problem happen in the first place?
That changes the entire conversation.
The Human Cost of a Denial
A denied claim does not look dramatic.
There is no ambulance.
No ICU.
No family gathered around a hospital bed.
Just a little red status on a computer screen.
Denied.
But behind that little word is a human being.
A biller.
A coder.
A practice manager.
A physician.
Someone has to fix it.
And that person already has a full workload.
This is why I think we have underestimated the human cost of
administrative waste.
Every unnecessary denial consumes human attention.
And human attention is one of the scarcest resources in
healthcare.
Physicians Don't Need More Software
Let me say something that may annoy the health-tech
industry.
Physicians do not need another dashboard.
They need fewer problems.
They do not want to log into another platform at 7:30 p.m.
to admire a beautiful visualization of their denial rate.
They want to know:
What is going wrong?
Why?
What do I need to do?
And can you help me prevent it from happening again?
That is the difference between technology theater and
useful technology.
AI Has a Role. But It Is Not the Role Everyone Thinks.
I am bullish on AI.
But I am skeptical of AI theater.
Putting the word “AI” in front of a workflow does not make
the workflow intelligent.
AI should not simply make bad processes faster.
That would be like putting a turbocharger on a car with no
steering wheel.
Very impressive.
Very fast.
Still a terrible idea.
The better use of AI is validation.
Look at the information.
Compare the relationships.
Find inconsistencies.
Identify risk.
Explain the problem.
Escalate the exception.
Let the human decide.
That is where AI can become genuinely useful.
What OnnX Is Trying to Do Differently
The goal is not to replace physicians.
It is not to eliminate experienced billing professionals.
It is not to create a mysterious black box that tells a
clinic:
“Trust the AI.”
The goal is much more practical.
Connect clinical reality with payer requirements before
the claim leaves the practice.
That means looking for problems such as:
Diagnosis and procedure mismatches.
Documentation gaps.
Modifier problems.
Medical-necessity concerns.
Authorization inconsistencies.
Payer-specific requirements.
Potential duplicate claims.
Coding anomalies.
The exact rules will vary by specialty, payer and service.
That is precisely why a rigid checklist is not enough.
The system has to understand context.
Three Expert Lessons
Expert Lesson One: Think in Systems
Patient-safety leaders increasingly emphasize systems
thinking.
That means resisting the temptation to reduce an event to:
“Someone made a mistake.”
People make mistakes.
Good systems anticipate that.
The question becomes:
What defenses were supposed to catch the mistake?
That same thinking belongs in revenue-cycle management.
If one employee enters incorrect information and the entire
claim becomes vulnerable, the system has a weakness.
Do not simply retrain the employee.
Fix the defense.
Expert Lesson Two: More Alerts Do Not Equal More Safety
Healthcare technology has a strange addiction to alerts.
Alert.
Alert.
Alert.
Alert.
Eventually the physician develops a new clinical condition:
alert fatigue.
The same thing can happen in billing.
If your software flags 500 things, congratulations.
You have created 500 things for someone to ignore.
The better goal is high-value intervention.
Tell the team what matters.
Tell them why.
Tell them what action is available.
Then get out of the way.
Expert Lesson Three: Technology Works Best With Humans
The future should not be:
AI versus physicians.
It should be:
AI plus physicians.
Technology can process enormous amounts of information.
Humans understand nuance, context and consequences.
A good system knows when to automate.
A better system knows when to stop and ask for help.
That principle matters enormously in healthcare.
The Glenda Dorton Lesson
Think about what Glenda's family is asking for.
Not revenge.
Not a public spectacle.
Not punishment for its own sake.
They want an explanation.
They want accountability.
And most importantly:
They want it not to happen again.
That is the essence of quality improvement.
And it should be the essence of healthcare technology.
If the technology identifies an error but nobody learns from
it, the system has failed twice.
First, it failed to prevent the problem.
Second, it failed to improve afterward.
The Most Dangerous Phrase in Healthcare
I would nominate:
“That's just how it works.”
We say it about billing.
We say it about prior authorization.
We say it about denials.
We say it about documentation.
We say it about administrative work.
We say it about physician burnout.
We say it about payer rules.
Eventually, dysfunction becomes tradition.
And tradition gets mistaken for necessity.
It isn't.
Five Questions Every Clinic Owner Should Ask
1. Where are our errors first created?
Not where they are discovered.
Where are they created?
Those are often different places.
2. Where are errors first detected?
If the answer is “after the payer denies the claim,” you
have a problem.
3. How many people touch a claim?
Every handoff creates potential friction.
4. What information is repeatedly re-entered?
Repeated data entry is an invitation to inconsistency.
5. What could we know earlier?
That may be the most important question of all.
A Practical Step-by-Step Revenue-Cycle Audit
You do not need a $500,000 consulting project.
Start with ten claims.
Pick ten recent claims.
Follow each one from:
Registration → Encounter → Documentation → Coding → Claim
→ Clearinghouse → Payer → Payment
Write down every handoff.
Then ask:
Where did someone manually enter information?
Where did someone reinterpret information?
Where did someone copy information?
Where did someone correct information?
Where did someone wait?
Where did someone have to call someone else?
You will probably find more friction than expected.
Step Two: Find the Repeat Offenders
Look at your last 100 denials.
Do not simply count them.
Group them.
Eligibility.
Authorization.
Coding.
Documentation.
Medical necessity.
Modifier.
Payer policy.
Data entry.
Now ask:
Which three categories account for the largest share?
That is where you start.
Step Three: Follow the Problem Upstream
Suppose 30% of your denials involve documentation.
Do not immediately tell the billing department to work
harder.
Ask:
Why is documentation incomplete?
Is the template confusing?
Is the physician unaware of a payer requirement?
Is the workflow too slow?
Is information captured in another system?
Is the problem specialty-specific?
The denial is the symptom.
Find the disease.
Step Four: Automate the Boring Stuff
Computers are very good at repetitive comparison.
Humans are good at judgment.
Use technology for things like:
matching
checking
flagging
sorting
prioritizing
Use humans for:
clinical judgment
complex exceptions
interpretation
communication
accountability
That division of labor makes much more sense than “AI
replaces everyone.”
Step Five: Measure What You Prevented
This is where many practices make a mistake.
They measure:
How many claims did we submit?
How many denials did we work?
How much money did we collect?
Those are useful.
But add:
How many problems did we catch before submission?
That number tells you whether your system is becoming
proactive.
Metrics That Actually Matter
Clean-Claim Rate
How often does the claim pass the first time?
Useful.
But incomplete.
Denial Rate
Important.
But even more important is why the claim was denied.
Days in A/R
A basic financial health indicator.
Rework Rate
How often must someone touch a claim again?
This is an underrated metric.
First-Pass Resolution
How often does the problem get resolved without multiple
cycles?
Upstream Detection Rate
How many potential problems were identified before
submission?
I believe this metric deserves much more attention.
The Billing Version of a Near Miss
Healthcare safety has a valuable concept:
near miss.
Something almost went wrong, but somebody caught it.
Revenue cycle should adopt the same mindset.
A claim that almost went out with a major error but was
stopped is not a failure.
It is a success.
It means the system worked.
That is an important cultural change.
Instead of hiding errors, measure how effectively you catch
them.
What Not to Do
Don't Fire Your Billing Company Tomorrow
Maybe the billing company is the problem.
Maybe it isn't.
Find the root cause first.
Don't Buy Five More Platforms
Technology fragmentation is already bad enough.
Don't Automate Everything
Some things require judgment.
Don't Trust AI Blindly
AI can be wrong.
Don't Blame the Physician Automatically
Physicians are often given incomplete or poorly designed
workflows and then blamed for the resulting data.
Don't Measure Activity Instead of Outcomes
A busy billing department isn't necessarily an effective
billing department.
A Little Humor About Healthcare Technology
We have built a remarkable healthcare ecosystem.
A physician can order a test from a computer.
The patient can receive a text message.
The insurance company can send an electronic authorization.
The EHR can create a beautiful note.
The clearinghouse can transmit a claim in milliseconds.
And then someone prints a spreadsheet and highlights a
denial with a yellow marker.
Progress.
Sometimes healthcare innovation feels like installing Wi-Fi
in a building and congratulating ourselves while the plumbing is still leaking.
The problem isn't that healthcare lacks technology.
The problem is that technology doesn't automatically
create a reliable system.
The Legal and Compliance Reality
This is where the conversation gets serious.
Healthcare organizations need to understand that automation
does not eliminate accountability.
If an AI system flags or influences a billing decision, the
organization still needs appropriate controls.
That means thinking about:
Audit trails
Data security
Access controls
Coding accuracy
Documentation
Medical necessity
Payer requirements
Fraud and abuse
Human oversight
Vendor accountability
Data retention
A black-box system that cannot explain why it made a
recommendation is a poor fit for high-stakes healthcare operations.
The more consequential the decision, the more important
transparency becomes.
Ethical Considerations
There is an ethical difference between:
assisting a professional
and
quietly replacing professional judgment.
AI should make the important information easier to see.
It should not make accountability harder to understand.
A physician or billing professional should be able to ask:
What did the system see?
Why did it flag this?
What information did it use?
What should I review?
What happens if the recommendation is wrong?
Good healthcare AI should make those questions easier to
answer.
Myth Buster
Myth: More automation means fewer errors.
Reality: Automation can accelerate errors if the
underlying process is wrong.
Myth: The billing department owns the revenue cycle.
Reality: Revenue-cycle performance begins upstream
with clinical documentation, registration, coding and workflow design.
Myth: A denial is a billing problem.
Reality: Many denials are symptoms of upstream
information problems.
Myth: AI will replace medical billers.
Reality: The more likely near-term opportunity is
AI-assisted validation, prioritization and workflow support.
Myth: Physicians don't need to understand billing.
Reality: Physicians do not need to become coders, but
they benefit from understanding how documentation affects claims and revenue.
Myth: The most advanced AI will win.
Reality: The most useful AI may be the technology
that solves a boring problem reliably.
Recent News: Why This Story Matters Right Now
The Nashville incident is still developing.
Current reporting says four joint-replacement patients
were affected, and the hospital has now acknowledged a pharmacy error. The
hospital says it has identified the cause and implemented corrective
safeguards. Tennessee authorities are investigating.
CBS News reported that the hospital had self-reported the
event to state regulators and launched an investigation.
The human story remains Glenda Dorton.
Her family says the 72-year-old went in for a knee
replacement and emerged with devastating neurological injury. Her husband, Marvin
Dorton, has remained at her bedside.
Her family is not asking the public to destroy someone's
career.
They are asking the healthcare system to understand what
happened.
That distinction is powerful.
It is also the foundation of modern patient safety.
The Bigger Healthcare Innovation Opportunity
Here is my prediction.
The next generation of healthcare technology will move from:
Automation
to
Validation
to
Prediction
to
Prevention
The first generation asked:
Can software do this task?
The second asked:
Can AI do this task?
The better question is:
Can the system recognize when something is about to go
wrong?
That is a much more interesting problem.
The Future of Medical Billing May Look Less Like Billing
Imagine a system that understands:
The patient.
The encounter.
The documentation.
The diagnosis.
The procedure.
The payer.
The authorization.
The coding rules.
The historical patterns.
Then imagine that system quietly says:
“Something doesn't line up.”
Not:
“Error 47291.”
Not:
“Please consult the 87-page payer manual.”
Just:
“This claim may fail because the documentation does not
clearly support the procedure under this payer's requirements. Review before
submission.”
That is useful.
That is actionable.
And most importantly:
that is early.
Why OnnX Exists
This is the problem I am trying to solve with OnnX.
I believe small and medium-sized physician practices deserve
better technology.
They should not need a massive health-system budget to gain
visibility into their revenue cycle.
They should not need five vendors to understand why claims
are failing.
They should not need physicians spending their evenings
playing detective.
And they should not have to accept:
“That's just how billing works.”
My thesis is that clinical data quality, coding accuracy
and revenue-cycle performance are connected.
If we improve the information before the claim is submitted,
we have a better chance of improving everything downstream.
What I Would Do If I Owned a Clinic
I would start small.
I would take 100 recent claims.
I would identify the top five failure patterns.
I would trace each one upstream.
Then I would ask:
Could technology have caught this earlier?
If yes, automate it.
If no, redesign the workflow.
If it requires judgment, escalate it.
If nobody owns the problem, assign ownership.
Then measure the result.
Do that repeatedly.
That is how transformation actually happens.
Not with a giant PowerPoint.
Not with a $2 million AI strategy.
One problem at a time.
The Most Important Metric May Be Boring
Here's another contrarian thought.
The most valuable healthcare technology may not produce the
most impressive demo.
It may produce fewer:
phone calls
corrections
denials
alerts
duplicate entries
manual reviews
after-hours messages
unnecessary meetings
Boring is underrated.
If your software makes the healthcare day boring in the best
possible way, it may be doing something very important.
Three Questions for Physicians
If you're a physician owner, ask your team:
What keeps coming back?
What takes too long?
What do we keep fixing manually?
Those questions uncover more opportunities than asking:
“What AI should we buy?”
Three Questions for Healthcare Founders
If you're building health tech, ask yourself:
What painful problem am I actually solving?
Where does information become unreliable?
What happens when my AI is wrong?
If you cannot answer those questions, you probably aren't
ready to automate the workflow.
Three Questions for Investors and Healthcare Leaders
Ask:
Does the technology reduce complexity?
Does it reduce human workload?
Does it improve decision quality?
If the answer to all three isn't yes, be careful.
A technology can have beautiful metrics and still make
healthcare harder.
What Glenda's Story Leaves Us With
There is something haunting about the simplicity of this
story.
Glenda Dorton wanted to fix her knee.
She was an avid gardener.
She had been putting off the surgery because she did not
want the recovery period, according to her family.
She went to Nashville.
Her husband Marvin was beside her.
She expected to recover.
Instead, her family is now wondering how much of her
previous life can be recovered.
That is not an abstract patient-safety statistic.
That is a person.
And perhaps that is exactly why the story matters.
Because every healthcare workflow eventually reaches a
person.
Even a claim.
The Lesson for Every Physician
Do not ask only:
“Did we provide good care?”
Also ask:
“Did our system make good care easier or harder?”
Those are different questions.
A brilliant physician working inside a badly designed system
can still encounter preventable failures.
A good billing team working with fragmented information can
still produce denials.
A highly trained nurse can still miss an alert buried among
30 irrelevant alerts.
A talented coder can still be handed incomplete
documentation.
Healthcare is a team sport.
But teams need good systems.
The Lesson for Every Clinic Owner
Your revenue cycle is not something that happens after
medicine.
It is part of the operational anatomy of your practice.
Clinical information becomes financial information.
Documentation becomes coding.
Coding becomes a claim.
The claim becomes cash.
Or denial.
Or rework.
Or delay.
That means billing begins much earlier than most
practices think.
The Lesson for Healthcare Founders
Don't build another tool that merely watches the problem.
Build something that helps prevent it.
Don't celebrate another dashboard.
Celebrate fewer unnecessary clicks.
Don't promise to replace humans.
Show how you make humans better.
And don't lead with AI.
Lead with the problem.
AI is the engine.
The patient's problem is the destination.
Final Thoughts: Healthcare Doesn't Need More Complexity
Glenda Dorton's story is painful.
But her family's response offers a powerful lesson.
They want answers.
They want accountability.
And they want the system to learn.
That is exactly what healthcare should do after failure.
Learn.
Not hide.
Not blame.
Not move on.
Learn.
The same philosophy should apply to medical billing.
When a claim fails, don't simply fix the claim.
Ask why.
When a denial repeats, don't simply work the denial.
Ask why.
When staff keep correcting the same problem, don't simply
tell them to be more careful.
Ask why.
Because the goal is not to build a system where perfect
people never make mistakes.
The goal is to build a system that catches imperfect
human mistakes before they become expensive, harmful or irreversible.
That is what patient safety teaches us.
That is what good technology should teach us.
And that is what I believe the future of medical billing
should look like.
Get Involved: Don't Just Read This. Challenge It.
Here is my question for physicians, clinic owners and
healthcare leaders:
What is one recurring problem in your practice that
everyone has accepted as “just the way healthcare works” even though it
shouldn't be?
Is it:
Denials?
Prior authorization?
Documentation?
Coding?
Payer rules?
Administrative overload?
Too many systems?
Tell me in the comments.
I genuinely want to know.
Because your answer may be the problem another physician has
been quietly struggling with for years.
Comment with the one workflow you would eliminate
tomorrow if you had the power to do it.
Share this article with a physician, practice owner or
healthcare leader who needs to rethink the difference between processing
problems and preventing them.
And if you believe healthcare technology should make good
people better rather than simply make broken processes faster, join the
conversation.
Three Things Worth Acting On Today
First: Find the problem you keep fixing.
If you fix it every week, it probably deserves a
system-level solution.
Second: Move detection upstream.
The earlier you catch an error, the easier it usually is to
correct.
Third: Stop confusing more technology with better
healthcare.
The best technology is often the technology that quietly
removes work from someone's day.
Continue the Conversation
Healthcare innovation should not end with another software
demo.
It should begin with a better question.
What could we prevent if we understood the workflow
better?
For additional perspectives on healthcare operations,
medical technology, medical billing, entrepreneurship and innovation,
explore:
Website:
Dr. Daniel Cham
Podcast:
The
Health Momentum Podcast on Spotify
YouTube:
Dr. Cham
on YouTube
Facebook:
Dr.
Cham on Facebook
Knowledge is useful only when it changes what we do.
Start learning. Question the old workflow. Build something better.
Free Resource
Check the Featured section of my LinkedIn profile for
a free resource designed to help physicians and healthcare leaders think
differently about healthcare operations and innovation.
No signup is required.
PS: The free resource is already waiting in Featured on
LinkedIn.
If this article made you rethink how information moves
through your practice, repost it.
One physician seeing this conversation today could help
another practice avoid tomorrow's problem.
About the Author
Dr. Daniel Cham is a physician, healthcare consultant
and medical-technology entrepreneur whose work focuses on the intersection of healthcare
management, medical technology, clinical workflows and medical billing.
He is the founder of OnnX, an AI-powered medical
billing SaaS focused on helping small and medium-sized physician practices
improve billing accuracy, identify revenue-cycle problems earlier and reduce
unnecessary administrative friction.
Dr. Cham's perspective is grounded in a simple idea:
Healthcare technology should solve real problems for real
people—not simply add another layer of technology to an already complicated
system.
Disclaimer
This article is intended for general educational and
informational purposes and should not be interpreted as medical, legal,
coding, compliance, financial or professional advice. The Nashville
medication-error investigation described above remains ongoing, and some
clinical details have been reported differently by various sources. Readers
should consult appropriately qualified professionals for guidance concerning
their individual circumstances.
References
1. CBS News — Nashville hospital medication mix-up.
Current national reporting describes four patients affected during
joint-replacement procedures and the ongoing Tennessee investigation.
2. FOX 17 Nashville — Glenda Dorton's family story.
Local reporting provides the most detailed account of Glenda Dorton, her
husband Marvin Dorton, daughter-in-law Kristina Dorton and the family's desire
for answers rather than individual blame.
3. WSMV Nashville — Hospital response and investigation.
The local NBC affiliate reports on the hospital's response, the four affected
patients and the state investigation.
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