Your Clinic Is Treating Patients. Who Is Treating Your Billing System?

The uncomfortable truth about medical billing, preventable denials, payer complexity, and why the next healthcare revolution may happen outside the examination room.

“Patients should not need a portal password, a billing manual or medical training to find and understand a health plan’s prior authorization practices.” — Dr. Willie Underwood III, President, American Medical Association

 

Three-quarters of providers surveyed say claim denials are increasing. But what if the industry is asking the wrong question?

The Patient Went Home. The Billing Problem Stayed.

Let's start with a familiar scene.

A physician sees a patient.

The examination goes well. The diagnosis is documented. The treatment is appropriate. The patient leaves with a plan.

Everyone did their job.

Well, almost everyone.

Three weeks later, the billing department discovers that the claim has been denied.

Why?

Perhaps the insurance was inactive. Maybe authorization was missing. Perhaps the wrong modifier was used. Or the payer decided that the service was not covered.

Now the practice begins its second job.

The biller opens the claim. Someone checks the payer portal. Another person reviews the documentation. The physician may be asked to clarify something that happened weeks earlier.

A phone call follows.

Then another.

Eventually, the claim may be corrected, appealed, resubmitted, or written off.

Congratulations. The patient encounter is over, but the administrative encounter has just begun.

And unlike the patient, the claim doesn't have an appointment scheduled for follow-up.

This is the strange economics of medical practice.

A physician can spend years learning how to diagnose and treat disease. Yet a seemingly minor administrative error can create weeks of additional work.

We have sophisticated systems for treating patients, but many practices still manage their revenue using disconnected workflows, manual follow-ups, and institutional memory.

We would never consider that an acceptable standard for clinical care.

Why have we accepted it for the business operations that sustain clinical care?

Here's my contrarian proposition:

The medical billing industry doesn't need another generation of software that is better at fixing yesterday's mistakes. It needs infrastructure that helps prevent tomorrow's mistakes.

And perhaps the most expensive question a clinic owner can ask is not, "How many claims did we collect?"

It is:

"Why are we still making the same mistakes?"

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The Great Medical Billing Illusion: More Billing Does Not Mean Better Billing

Healthcare has developed an interesting habit.

When reimbursement becomes difficult, the first instinct is often to add another layer.

Another biller.

Another vendor.

Another clearinghouse.

Another dashboard.

Another spreadsheet.

Another portal login.

At some point, someone should probably invent a system that remembers all the passwords for the systems that were purchased to solve the password problem.

There is humor in that, but the underlying issue is serious.

More tools do not automatically create a better process.

A practice can have a modern EHR, an experienced billing company, a clearinghouse, an authorization vendor, and a collection service, yet still experience substantial revenue leakage.

Why?

Because having multiple systems is not the same as having an integrated system.

Consider the patient journey:

1. Registration.
2. Insurance verification.
3. Scheduling.
4. Prior authorization.
5. Patient encounter.
6. Clinical documentation.
7. Coding.
8. Charge capture.
9. Claim preparation.
10. Claim submission.
11. Payer adjudication.
12. Payment posting.
13. Reconciliation.

These are not thirteen unrelated administrative activities.

They are connected stages of one financial process.

Yet information often becomes fragmented as it moves between people, departments, vendors, and software platforms.

The front desk knows one thing.

The authorization team knows another.

The physician documents the encounter.

The coder interprets the documentation.

The biller submits the claim.

The payer applies its rules.

And somewhere along the way, a small piece of information goes missing.

The practice discovers it weeks later.

The problem is not necessarily a lack of effort. It may be a lack of continuity.

That distinction is important.

An experienced biller can correct a claim. But if the system repeatedly produces the same error, even the best biller becomes an expensive repair technician.

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The Denial Industry: Are We Solving the Problem or Building a Business Around It?

Here's a question that may make some people uncomfortable.

Has the healthcare industry become too accustomed to denials?

We discuss denial management as though it were an unavoidable department of every medical practice.

We measure denials.

We categorize denials.

We appeal denials.

We hire people to work denials.

We purchase software to prioritize denials.

We build entire business models around recovering money from denied claims.

Of course, legitimate denials must be investigated and challenged.

But there is a difference between managing an unavoidable problem and accepting a preventable one.

A denial is an outcome. It is not necessarily the root cause.

Imagine a clinic that experiences the same authorization-related denial every month.

The billing company becomes very efficient at identifying the denial.

The staff becomes very efficient at calling the payer.

The practice becomes very efficient at submitting appeals.

But nobody changes the scheduling workflow that caused the authorization to be missed.

Is that operational excellence?

Or simply getting better at repeating the same mistake?

This is where I would challenge conventional revenue cycle management.

The industry has become very good at measuring what went wrong after the fact.

The greater opportunity is identifying what is about to go wrong before the claim leaves the practice.

Prevention should be a core operating principle, not an optional software feature.

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The Numbers: The Industry Is Sending a Warning

The latest findings from Experian Health's 2026 State of Claims survey, released October 28, 2026, deserve attention.

The survey included 204 healthcare professionals involved in financial, billing, registration, reimbursement, claims, and collection operations.

Among the findings:

• 75% reported that claim denials were increasing.
• 40% said at least 10% of claims were denied.
• 79% said obtaining reimbursement had become more difficult.
• 78% reported more frequent changes in payer policies.
• 65% said submitting clean claims had become more difficult.
• 64% reported increasing reimbursement timelines.
• 89% identified denial reduction as a priority for 2026.

The most interesting finding may be the changing reasons behind denials.

Services not covered was the most frequently cited denial driver, identified by 53% of respondents among their top three reasons.

Frequently changing payer policies followed at 31%.

Coding inaccuracies and difficulty obtaining prior authorization were each identified by 25%.

These findings deserve careful interpretation. They represent survey responses, not a universal denial rate for every practice or specialty.

But they raise an important question.

If payer policies are becoming more complicated, reimbursement is becoming more difficult, and clean claims are harder to submit, should independent practices continue relying on the same fragmented administrative processes?

I don't think so.

The industry is changing. The operating model of the independent practice needs to change with it.

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A Rejection Is Not a Denial. And Neither Is an Excuse to Lose Visibility.

Let's talk about something that sounds technical but has very practical consequences.

A claim rejection and a payer denial are not the same thing.

Confusing them can waste time and send the wrong problem to the wrong person.

Claim rejection: The claim didn't get through the door.

A clearinghouse or electronic transaction validation process may identify problems such as:

• Missing demographic information.
• Invalid member identifiers.
• Incorrect provider information.
• Missing data elements.
• Invalid coding or formatting.
• Electronic transaction errors.

The claim generally needs correction before it can proceed through payer adjudication.

Payer denial: The claim reached the payer's adjudication process.

The payer determines that the claim is not payable under its applicable rules.

Possible reasons include:

• Coverage limitations.
• Medical necessity.
• Authorization requirements.
• Coding or documentation issues.
• Timely filing.
• Other contractual or reimbursement conditions.

The distinction matters because the solutions are different.

You do not solve a missing patient identifier by writing a clinical appeal.

And you do not solve a medical necessity dispute by simply resubmitting the same claim.

The electronic acknowledgment trap

There is another important detail.

A 999 acknowledgment provides information about transaction-level implementation and syntax validation.

A 277CA provides claim-level acknowledgment information.

Neither should be confused with final payment.

A successfully transmitted batch does not necessarily mean every claim was accepted.

A claim-level acceptance does not mean the payer has completed adjudication.

And adjudication does not necessarily mean payment has been received.

Think of it this way:

Sending a claim is like mailing an invoice. Receiving confirmation that it arrived does not mean the customer has paid it.

Practices need visibility across every stage.

Otherwise, claims can disappear into the administrative equivalent of a black hole.

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The Five Places Where Your Revenue Cycle Can Break

If I were reviewing the operations of a small or medium-sized medical practice, I would start with five areas.

Not because every problem originates there, but because these areas create important dependencies throughout the revenue cycle.

A. Registration: The first domino

A patient enters the practice.

The insurance card is scanned. Information is entered. The appointment is confirmed.

Everyone moves on.

But what happens if the information is incorrect?

A misspelled name, inaccurate date of birth, outdated insurance identifier, or incorrect subscriber relationship can create downstream complications.

Practical improvement:

• Standardize patient registration.
• Verify essential demographic information.
• Check insurance details.
• Reduce duplicate patient records.
• Establish a correction process.
• Ensure updated information flows into the billing system.

Do not underestimate the financial consequences of seemingly small data errors.

B. Eligibility: Coverage today is not coverage last month

Insurance information changes.

A patient may have changed employers, switched plans, lost coverage, or acquired secondary insurance.

An old eligibility response is not necessarily useful for a new encounter.

Practical improvement:

• Verify eligibility close to the date of service.
• Review relevant benefits.
• Identify coordination-of-benefits issues.
• Document the response.
• Escalate unresolved discrepancies.

One important qualification: an eligibility response is not a guarantee of payment.

It is information that helps the practice make better administrative decisions.

C. Authorization: The appointment is not the approval

This deserves special attention.

A scheduled procedure does not automatically mean authorization requirements have been satisfied.

An authorization must match the relevant patient, service, provider, dates, and applicable limitations.

A common scenario:

A patient is added to the schedule at the last minute.

The scheduling team confirms the appointment.

The authorization workflow is bypassed.

The physician performs the procedure.

The claim is denied.

The billing department discovers the issue weeks later.

Now everyone is asking what happened.

The better question is why the system allowed the appointment to move forward without the required administrative checkpoint.

Practical improvement:

• Identify services requiring authorization.
• Track approval and expiration dates.
• Match approvals to scheduled services.
• Flag changes in treatment plans.
• Establish escalation procedures.

A process should not depend entirely on someone remembering to check a sticky note.

D. Documentation and coding: The clinical story must match the claim

The medical record and the claim must tell a consistent story.

Clinical documentation must support the services reported.

Coding must accurately represent the encounter.

Potential problems include:

• Missing documentation.
• Unsupported codes.
• Incorrect modifiers.
• Inconsistent diagnoses.
• Incomplete medical necessity information.
• Incorrect provider details.

Practical improvement:

Use coding validation, appropriate documentation checks, current references, and physician clarification when needed.

Automation can identify potential inconsistencies.

It should not invent clinical facts or encourage unsupported coding.

E. Submission and reconciliation: The story does not end when you click Submit

Many practices focus heavily on getting claims out the door.

But submission is only one event in a longer process.

What happens afterward?

Was the claim acknowledged?

Was it accepted?

Was it adjudicated?

Was it paid correctly?

Was the payment posted?

Was the remaining balance reconciled?

Practical improvement:

Establish clear monitoring for outstanding claims, acknowledgments, remittances, underpayments, and unresolved denials.

A claim should have a visible status and a responsible owner.

Otherwise, the practice is not managing its revenue cycle.

It is hoping that something happens.

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The Most Important Question: Why Did It Happen?

Let's consider a simple example.

A payer denies a procedure because authorization was not obtained.

The conventional response is to investigate whether a retroactive authorization is possible.

That is reasonable for the individual claim.

But let's go further.

Why was authorization missing?

Because nobody requested it.

Why?

Because the appointment was added on the same day.

Why did the scheduling system allow that appointment to bypass authorization review?

Because the scheduling and authorization processes were disconnected.

Why were they disconnected?

Because the practice had never established a rule requiring the two workflows to communicate.

Now we have found something worth fixing.

The root cause is not necessarily an employee's mistake.

It may be a process-design failure.

This is the logic of root cause analysis.

The goal is not to blame the front desk, the biller, the physician, or the software.

The goal is to understand why the system produced the outcome.

I would rather see a practice spend an hour fixing a recurring workflow problem than spend the next twelve months becoming exceptionally good at appealing the same denial.

Fix the process once. Stop paying for the same mistake repeatedly.

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Three Expert Perspectives: The Industry Is Telling Us Something

The most useful insights often come from people approaching the same problem from different directions.

Three recent perspectives illustrate where healthcare administration is heading.

Expert 1: Mindy Fortson — Chief Operating Officer, Experian Health

In Experian Health's October 28, 2026, State of Claims announcement, Mindy Fortson highlighted the growing pressure on providers and the importance of accurate data and technology that can identify problems upstream.

Her central point was that waiting to address denial problems has a cost.

Every denial creates additional work, delays reimbursement, and may put revenue at risk.

My interpretation:

Technology should not simply help the billing team work faster.

It should help the practice identify problems earlier.

The distinction between accelerating administrative work and preventing unnecessary work is significant.

Expert 2: Dr. Willie Underwood III — President, American Medical Association

The AMA's October 26 discussion of payer prior authorization disclosures highlighted a problem that physicians and patients know well.

Information may technically be available but practically difficult to locate or understand.

Dr. Underwood's observation about patients needing a portal password, billing manual, or medical training to understand payer authorization practices captures the problem perfectly.

My interpretation:

Information without accessibility is not meaningful transparency.

Healthcare technology should make complex requirements easier to understand and act upon.

It should not create another administrative obstacle.

Expert 3: Hannah Neprash and Michael Barnett — Researchers examining Medicare's WISeR pilot

Research published in JAMA and discussed by the University of Minnesota on October 26, 2026, examined the early effects of Medicare's WISeR prior authorization pilot.

The study found that use of targeted services declined in participating states relative to comparison trends.

Epidural steroid injections declined by approximately 15%, while other targeted services declined by approximately 20%.

The researchers emphasized that the study could not determine whether the care that was not delivered was unnecessary or medically appropriate.

Hannah Neprash, the study's lead author, and Michael Barnett, a co-author, highlighted the need to understand the effects on patients, clinicians, and healthcare spending.

My interpretation:

Automation can change healthcare behavior.

But a reduction in utilization is not automatically evidence of better care.

We need to evaluate outcomes, not celebrate activity reduction as a measure of success.

That principle applies to both payer-side and provider-side AI.

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AI in Medical Billing: Don't Replace the Biller. Replace the Repetitive Work.

There is a lot of excitement around artificial intelligence.

Some people believe AI will eliminate billing departments.

Others believe it will revolutionize every aspect of revenue cycle management.

I would take a more measured position.

AI can be useful.

But AI is not magic, and a poorly designed workflow does not become intelligent simply because someone adds an AI label.

Imagine giving an automated system inaccurate patient information.

It may process that information very quickly.

Congratulations. You have automated the mistake.

This is why data integrity must come before automation.

There are several areas where AI-assisted workflows could be valuable.

Eligibility verification: Identify potential discrepancies before the encounter.

Authorization tracking: Flag missing or expiring approvals.

Coding validation: Identify inconsistencies that require review.

Claim readiness: Check for missing or questionable information before submission.

Denial intelligence: Identify recurring patterns and possible root causes.

Claim monitoring: Prioritize unresolved claims and follow-up activities.

Payment reconciliation: Help identify discrepancies between expected and received payments.

But human oversight remains essential.

Physicians must retain clinical judgment.

Qualified billing professionals must review complex issues.

Compliance personnel must evaluate applicable requirements.

And practices must understand how automated systems handle sensitive information.

The objective should be human expertise supported by intelligent automation.

Not human expertise replaced by an algorithm that nobody can explain.

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Myth Busters: Five Comfortable Beliefs That Deserve to Be Challenged

Myth 1: Medical billing problems are mostly coding problems.

Reality: Coding is only one part of the process. Eligibility, registration, authorization, coverage, documentation, and payer requirements also matter.

Myth 2: A clean claim guarantees payment.

Reality: A technically valid claim can still be denied because of coverage, authorization, medical necessity, or other reimbursement requirements.

Myth 3: Outsourcing eliminates billing problems.

Reality: Outsourcing can provide expertise and capacity. It cannot automatically correct weaknesses in the practice's internal workflows.

Myth 4: More software means better revenue cycle management.

Reality: Disconnected systems can create more complexity. Integration, data quality, and accountability matter more than the number of platforms.

Myth 5: AI will eliminate the need for experienced billers.

Reality: Routine work may become increasingly automated, but complex appeals, documentation interpretation, payer disputes, compliance, and operational judgment remain important.

My position is simple:

Automate the repetitive work. Elevate the human work. Fix the system that connects them.

 

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The Practical Playbook: Seven Steps to Stop Manufacturing Denials

So where should a clinic owner actually begin?

Not with a six-figure technology purchase.

Not with firing the billing company.

And certainly not with another spreadsheet that someone will forget to update.

Start with the process.

Step 1: Find out where the money is getting stuck

Review the last 90 days of claims.

Identify:

• Total claims submitted.
• Clearinghouse rejection rate.
• Initial payer denial rate.
• Days in accounts receivable.
• Claims older than 90 days.
• Authorization-related denials.
• Eligibility-related denials.
• Coding-related denials.
• Unresolved underpayments.

You cannot improve what you cannot see.

Step 2: Separate rejections from denials

Create separate categories.

Do not mix technical submission errors with payer adjudication decisions.

Assign each problem to the appropriate person or team.

Step 3: Find your top three recurring problems

Do not attempt to fix everything simultaneously.

Identify the three largest recurring sources of rework or financial exposure.

Look at frequency, financial impact, and how much staff time each problem consumes.

Step 4: Trace each problem upstream

Ask why it happened.

Was the patient information incorrect?

Was eligibility not checked?

Did scheduling bypass authorization?

Was documentation incomplete?

Did the claim validation process fail?

Find the earliest point where the problem could have been detected.

Step 5: Introduce preventive checkpoints

Build verification into the workflow.

Use appropriate system alerts, required fields, automated checks, and escalation procedures.

The goal is to make the correct process easier to follow than the incorrect one.

Step 6: Give every problem an owner

Accountability should be clear.

The front desk should not be expected to solve complex coding problems.

The billing team should not be responsible for correcting every clinical documentation issue.

Management must ensure that the entire process works together.

Step 7: Measure whether the changes actually work

Compare performance before and after implementation.

Are rejection rates improving?

Are authorization problems declining?

Is staff rework decreasing?

Are outstanding claims being resolved faster?

Are collections improving?

If the numbers do not improve, investigate why.

Do not confuse purchasing technology with achieving results.

A successful implementation is measured by operational improvement, not the number of software features purchased.

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The Metrics That Matter: Measure More Than Collections

Many practice owners focus on total collections.

That makes sense. Cash flow matters.

But collections are a lagging indicator.

By the time collections decline, the underlying operational problem may have existed for weeks or months.

A more useful approach combines financial, operational, and preventive measurements.

Metric	What it reveals
First-pass acceptance rate	How frequently claims pass initial submission checks
Clearinghouse rejection rate	Technical submission problems
Initial denial rate	Payer denials at first adjudication
Days in A/R	Time outstanding balances remain unpaid
Net collection rate	How effectively collectible revenue is recovered
Authorization denial rate	Problems involving authorization requirements
Denial overturn rate	Outcomes of appealed denials
Claim resolution time	Time required to resolve claim problems
Rework hours	Administrative effort spent correcting errors
Registration error rate	Accuracy of patient intake information

One important distinction: first-pass acceptance, first-pass clean claim rate, and denial rate are not interchangeable.

Practices should define each metric consistently before comparing performance.

A simple example

Imagine a hypothetical three-provider clinic.

It submits 1,000 claims per month.

Its initial rejection rate is 7%.

Its initial payer denial rate is 10%.

These are illustrative figures, not industry benchmarks.

Management identifies recurring registration and authorization problems.

The practice introduces improved verification and scheduling checkpoints.

After implementation, it measures the same metrics over several months.

Even if collections do not immediately increase, a reduction in repeated rework and unresolved claims may demonstrate meaningful operational improvement.

The point is not to chase a perfect number.

It is to understand what is happening inside the practice.

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The Pitfalls: What Happens When Technology Becomes the Strategy?

Technology can solve problems.

It can also create new ones.

Here are seven mistakes I would avoid.

1. Buying software before mapping the workflow.

You may end up automating a broken process.

2. Assuming the billing vendor owns the entire revenue cycle.

The practice still controls important activities such as registration, scheduling, documentation, and clinical operations.

3. Treating every denial as recoverable.

Some denials cannot be overturned. Prevention and timely corrective action are essential.

4. Ignoring payer-specific rules.

Requirements can differ by payer, plan, service, and provider.

5. Automating without validating the data.

Fast errors are still errors.

6. Ignoring the people doing the work.

Experienced billers and administrative staff often know exactly where the process breaks.

Ask them before redesigning their workflow.

7. Assuming a dashboard is the same as intelligence.

A dashboard tells you what happened.

Useful operational intelligence helps explain why it happened and what should happen next.

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Legal and Regulatory Considerations: AI Does Not Get a Compliance Vacation

Healthcare technology operates within a complex regulatory environment.

The convenience of automation does not remove existing obligations.

HIPAA and protected health information

Practices considering AI or automation must evaluate:

• Business associate agreements.
• Access controls.
• Authentication.
• Encryption.
• Audit logging.
• Data retention.
• Vendor security.
• Breach response.
• Appropriate use of protected health information.

A technology vendor should not receive access to patient information simply because its product looks impressive in a demonstration.

CMS interoperability and prior authorization requirements

The CMS Interoperability and Prior Authorization Final Rule, CMS-0057-F, introduces requirements for specified payers involving prior authorization processes, APIs, and data exchange.

Important provisions have implementation dates in 2026 and 2027, depending on the requirement and payer category.

Practices should understand which payer obligations apply and how they may affect their own workflows.

Coding and billing integrity

AI-generated coding suggestions must be reviewed appropriately.

Claims must accurately reflect services performed and documentation supporting those services.

Practices should maintain processes addressing:

• Coding accuracy.
• Medical necessity.
• Documentation integrity.
• Modifier use.
• Timely filing.
• Payer contract requirements.
• Overpayment identification and reporting.

Automation does not transfer the provider's legal or compliance responsibilities to a software company.

AI governance

Before introducing an AI tool, ask:

1. What information does it process?
2. Where is that information stored?
3. How are recommendations generated?
4. Can a qualified person review the output?
5. Can errors be identified and corrected?
6. What happens when the system produces an incorrect result?
7. Who is accountable?

If nobody can explain how an automated decision affects a claim, the practice has a governance problem.

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Ethical Considerations: The Revenue Cycle Must Not Become the Clinical Decision-Maker

There is a line that healthcare technology should never cross.

Financial efficiency must not replace clinical judgment.

An algorithm should not encourage unsupported documentation.

A coding system should not manufacture diagnoses.

A payer authorization model should not be assumed to make clinically appropriate decisions simply because it operates quickly.

The ethical principles are straightforward:

• Accuracy: Clinical and billing information must reflect what actually happened.
• Transparency: People should understand when automated systems influence decisions.
• Accountability: Qualified professionals must retain appropriate oversight.
• Privacy: Patient information must be protected.
• Fairness: Systems should be evaluated for potential bias.
• Patient access: Administrative efficiency must not become an unnecessary barrier to appropriate care.

The ultimate purpose of improving revenue cycle management is to support sustainable medical practices.

Not to turn every patient encounter into a financial optimization exercise.

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Recent News: Three Developments That Should Make Clinic Owners Pay Attention

The final week of October 2026 offers an interesting snapshot of where healthcare administration is heading.

October 28: Experian Health reports continued denial pressure

Experian Health released its 2026 State of Claims survey.

Three-quarters of respondents reported increasing denials, while 89% identified denial reduction as a priority.

The report also found growing interest in AI and automation.

Why it matters: Providers recognize that existing claims processes need improvement. The opportunity is to focus on prevention rather than simply accelerating rework.

October 26: The AMA challenges payer transparency

The American Medical Association highlighted difficulties locating and understanding health plans' public prior authorization information.

Some disclosures were buried behind provider portals, while others contained incomplete or inconsistent information.

Why it matters: Better technology requires better information. A digital system that hides important requirements is not necessarily an improvement.

October 26: Medicare's WISeR pilot raises questions about AI and access

University of Minnesota researchers reported early findings from the Medicare WISeR prior authorization pilot.

The study identified declines in utilization of certain targeted services in participating states.

However, researchers could not determine whether the care forgone was medically unnecessary or appropriate.

Why it matters: AI-driven administrative efficiency must be evaluated against patient outcomes and access to care.

Together, these developments reveal an important tension.

Healthcare needs better automation, but automation must be transparent, accountable, and clinically responsible.

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The Future: From Medical Billing to Revenue Intelligence

Now let's consider a different possibility.

What if the future of medical billing is not really about billing?

What if it is about building a system that understands the financial and administrative requirements of a patient encounter from the beginning?

Imagine this workflow.

A patient schedules an appointment.

Insurance information is reviewed.

Eligibility is checked.

Potential authorization requirements are identified.

The appropriate staff member receives an alert when something needs attention.

The physician completes the encounter.

Documentation is reviewed for completeness.

Coding suggestions are generated and validated.

Potential claim errors are identified before submission.

The claim is transmitted.

Electronic acknowledgments are monitored.

Payer responses are tracked.

Payments are reconciled.

Recurring problems are analyzed.

The practice learns from each completed encounter.

This is a different operating philosophy.

Instead of treating each claim as an isolated transaction, the system maintains continuity across the revenue cycle.

The evolution of the medical biller

Will AI change medical billing jobs?

Absolutely.

But I would challenge the assumption that the future is simply about replacing billers.

Experienced medical billing professionals understand payer behavior, reimbursement rules, documentation requirements, and the practical realities of running a medical practice.

That knowledge is valuable.

The opportunity is to reduce repetitive administrative tasks so that experienced professionals can focus on:

• Complex claim resolution.
• Payer policy interpretation.
• Clinical documentation clarification.
• Root cause analysis.
• Compliance oversight.
• Revenue performance.
• Workflow improvement.

The biller of tomorrow may spend less time entering information and more time managing the integrity of the revenue cycle.

The next competitive advantage

For independent practices, the competitive advantage may not come from having the largest technology budget.

It may come from having the most reliable operational process.

A smaller clinic with connected workflows, accurate data, and clear visibility may operate more effectively than a larger organization with disconnected systems.

That is worth considering.

The future belongs not necessarily to the practices with the most software, but to those that know how to make their systems work together.

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Where OnnX Fits Into This Conversation

This brings me to why I am exploring this problem.

As a physician and healthcare entrepreneur, I have been interested in the relationship between clinical care, practice operations, and medical billing.

One observation keeps returning.

Revenue cycle problems are often visible at the end of the process but originate much earlier.

That is the thinking behind OnnX.

I am exploring an AI-powered medical billing approach designed around better workflow continuity, data validation, claim readiness, and revenue cycle visibility.

The intention is not to create another disconnected platform that adds work to an already complicated environment.

Nor is it to suggest that every existing billing company or practice management system should disappear.

The more interesting question is whether we can make the underlying process work better.

Can we identify problems earlier?

Can we reduce unnecessary manual intervention?

Can we help practices understand why claims fail?

Can we preserve experienced human oversight while automating repetitive work?

Can independent practices gain better control and visibility without having to rebuild their entire operations?

These are questions worth investigating.

I am particularly interested in the experience of independent physicians, clinic owners, administrators, and medical billers.

Because the people who experience these problems every day should help shape the next generation of solutions.

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Final Thoughts: Stop Paying People to Fix What Your System Should Have Prevented

Let me leave you with three thoughts.

First, a denial is not always a billing failure. Sometimes it is the financial consequence of a decision or omission made much earlier.

Second, adding more people and more software does not necessarily solve a broken process. Better information flow, accountability, and preventive controls may be more valuable.

Third, AI should make healthcare administration more intelligent, not simply more automated.

I believe independent medical practices deserve better infrastructure.

Physicians should spend more time practicing medicine and less time wondering whether a claim submitted three weeks ago has disappeared into a payer portal.

Experienced billers should spend more time applying their knowledge and less time repeatedly correcting preventable errors.

And clinic owners should have a clearer understanding of where their revenue is going, where it is getting stuck, and why.

The future of medical billing should not be about becoming better at chasing money.

It should be about building a system that makes the chase less necessary.

Perhaps the biggest innovation in medical billing will not be a faster way to submit a claim. It will be a smarter way to prevent the claim from going wrong in the first place.

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Frequently Asked Questions

Why do medical claims get denied?

Common reasons include coverage limitations, eligibility errors, missing authorization, coding inaccuracies, documentation deficiencies, medical necessity disputes, and timely filing issues.

The important step is identifying the underlying cause rather than treating every denial as an isolated billing problem.

What is the difference between a rejection and a denial?

A rejection generally occurs during electronic or administrative validation before the claim enters payer adjudication.

A denial occurs when the payer processes a claim or request and determines that payment will not be made under applicable requirements.

Each requires a different corrective approach.

Can AI prevent medical billing denials?

AI can help identify potential errors, validate information, monitor authorization requirements, and detect recurring patterns.

However, AI cannot guarantee payment or eliminate every denial.

Its effectiveness depends on data quality, workflow integration, accurate rules, and human oversight.

What causes revenue leakage in independent practices?

Revenue leakage can result from incomplete registration, eligibility problems, authorization failures, coding inaccuracies, missed charges, claim submission errors, underpayments, and ineffective follow-up.

Some problems are financial. Others are operational.

Many are connected.

Does outsourcing medical billing solve the problem?

Outsourcing can provide specialized expertise and administrative capacity.

However, outsourcing does not automatically correct weaknesses in a practice's registration, scheduling, documentation, or authorization processes.

The practice and its billing partner need clear accountability and visibility.

What should a small clinic measure first?

Start with first-pass acceptance, rejection rates, initial denial rates, days in accounts receivable, net collection rate, authorization-related denials, and unresolved claims.

Also measure staff time spent correcting errors.

A financial metric alone may not reveal the operational problem.

Will AI replace medical billers?

AI will likely automate more routine administrative activities.

However, experienced professionals will remain important for complex appeals, payer interpretation, documentation review, compliance, and operational decision-making.

The opportunity is to improve the work, not simply eliminate the workforce.

What is revenue intelligence?

Revenue intelligence is an approach to understanding and managing the financial process across the patient journey.

It combines data quality, workflow visibility, operational analysis, automation, and human judgment to identify problems earlier and improve revenue cycle performance.

How can a clinic improve billing without replacing its existing software?

Start by identifying workflow gaps and recurring problems.

Improve information flow between existing systems, establish preventive checkpoints, standardize responsibilities, and evaluate targeted automation.

Replacing everything is not always necessary.

What is the biggest lesson for physicians and clinic owners?

Stop looking at denials only as events that happen after claims are submitted.

Investigate the processes that created them.

The most effective solution may be several steps upstream.

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Get Involved: Help Shape the Future of Medical Billing

I want to hear from the people who understand this problem firsthand.

Here's my question:

If you could eliminate one recurring problem in your practice's revenue cycle tomorrow, what would it be?

Would it be:

• Insurance eligibility errors?
• Prior authorization delays?
• Coding and documentation inconsistencies?
• Repeated claim denials?
• Lack of visibility into outstanding payments?
• Too many disconnected billing systems?

Leave a comment. Your experience may help another physician or clinic owner recognize a problem they have been struggling with.

Share this article. More physicians, administrators, and billing professionals should be part of this conversation.

Join the discussion. Help challenge outdated assumptions and shape a more intelligent approach to healthcare administration.

If you believe the industry needs to move from reactive billing to preventive revenue intelligence, I would welcome your perspective.

P.S. Looking for something practical? Visit the Featured section of my LinkedIn profile for a free resource. No signup or email registration required.

If this perspective resonates, consider reposting to help other physicians and clinic owners rethink how billing affects their practices.

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About the Author

Dr. Daniel Cham, MD, MS is a physician, healthcare strategist, medical technology consultant, and entrepreneur focused on healthcare operations, medical billing, and innovation.

He is the founder of OnnX, an AI-powered medical billing initiative exploring how better data integrity, intelligent automation, and connected workflows can help independent medical practices address revenue cycle complexity.

His work focuses on practical solutions at the intersection of clinical practice, healthcare management, and emerging technology.

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Disclaimer / Important Note

This article is provided for general educational and informational purposes only. It should not be interpreted as medical, legal, coding, compliance, or financial advice. Readers should seek guidance from appropriately qualified professionals when evaluating specific situations or making operational decisions.

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Continue Exploring

Healthcare is evolving, and the conversations surrounding medical billing, artificial intelligence, practice management, and innovation should evolve with it.

Explore additional perspectives, practical ideas, and discussions about building better healthcare operations.

Connect: LinkedIn | Website | YouTube

Knowledge drives progress. Challenge what is familiar, question what is inefficient, and help build what comes next.

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References & Further Reading

1. Experian Health — 2026 State of Claims Survey — Reports increasing claim denials, changing payer policies, reimbursement challenges, and healthcare organizations’ perspectives on AI in claims management.
2. American Medical Association — Making Payers’ Prior Authorization Data Easier to Access and Understand — Examines the difficulty providers face accessing and interpreting health plans’ prior authorization information.
3. University of Minnesota — Medicare’s Prior-Authorization Pilot Test Led to an Immediate Drop in Use of Services — Examines research findings on Medicare’s WISeR pilot and the potential effects of prior authorization on healthcare service utilization.

4. Healthcare Saved a Stranger’s Life in Minutes. Why Does It Take Months to Fix a Broken Claim? — Explores why healthcare should apply the same prevention mindset to administrative systems that it applies to patient care.
5. The Hidden Cost of Medical Billing: Why Clinics Are Losing Revenue in Silence (and How AI Is Reshaping the Middle Layer of Healthcare) — Examines fragmented billing workflows, revenue leakage, and the potential role of AI in improving operational accuracy.
6. Your Billing Denial Has a Plot Twist: The Problem May Have Started Before the Claim Existed — Explains how billing failures can originate upstream in scheduling, registration, eligibility, authorization, and documentation.

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#MedicalBilling #RevenueCycleManagement #HealthcareAI #HealthcareInnovation #PhysicianEntrepreneur #IndependentMedicalPractice #MedicalPracticeManagement #ClaimDenials #DenialPrevention #HealthcareTechnology #ArtificialIntelligence #RevenueIntelligence #OnnX

 

The Denial Isn't the Problem. It's the Clue.

Healthcare Has a Billing Problem. Or Does It Have an Information Problem?

A provocative look at medical billing, AI automation, payer complexity, and why independent medical practices keep paying for mistakes they didn't necessarily make.

“They're able to review things faster, deny things faster, and we're still pretty manual in terms of our appeals efforts.” — Shawntea “Taya” Gordon, MBA, FACMPE, CEO of Atlas & Perpetua Healthcare Consulting, discussing the growing imbalance between payer automation and provider-side operations,

 

Here is an uncomfortable thought: What if the medical billing industry is getting very good at fixing the wrong problem?

A physician sees a patient. The examination is completed. The diagnosis is documented. The treatment plan is established. The patient leaves.

Then the real adventure begins.

The insurance information does not match. The authorization is missing. The claim gets rejected. The billing department opens another work queue. Someone calls the payer. Someone else searches the chart. Another person sends an appeal.

Congratulations. The practice has just created another administrative job without hiring anybody.

The physician delivered healthcare.

The billing team delivered detective work.

And somewhere between the examination room and the insurance company's payment department, the original transaction became an administrative obstacle course.

Sound familiar?

For independent physicians and small medical practices, this is more than an inconvenience. It affects cash flow, employee productivity, patient relationships, and the financial survival of the practice.

But I want to challenge the conventional explanation.

Perhaps healthcare does not have a billing problem. Perhaps it has an information problem that eventually becomes a billing problem.

And if that is true, we have been spending too much time fixing claims and not enough time examining why the information behind those claims keeps breaking.

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The uncomfortable truth: Your billing department may be cleaning up somebody else's mess

Let's start with a simple example.

A receptionist enters a patient's date of birth incorrectly.

One digit.

Not a complicated medical decision. Not a sophisticated coding question. Just one digit.

The eligibility response may not match. The claim may fail front-end validation. The billing team investigates. Someone corrects the information. The claim gets resubmitted.

How much did that one digit cost?

Not just the employee's time.

Consider the chain reaction:

• Registration has to be reviewed.
• Insurance information has to be checked.
• Eligibility may need to be verified again.
• The clearinghouse response has to be interpreted.
• The claim has to be corrected.
• The billing team has to resubmit it.
• Payment may be delayed.

One digit has become a small administrative production.

Now imagine that process repeated across hundreds of encounters.

This is why the cost of an error is often much larger than the cost of correcting the error.

And here is the contrarian part.

We should stop assuming that every billing problem belongs to the billing department.

A claim is the final output of an information chain.

The problem may have started at registration, scheduling, eligibility verification, authorization, clinical documentation, coding, or information exchange between systems.

The billing department may simply be the first place where somebody notices.

The person who discovers the error is not necessarily the person or process that created it.

That distinction matters.

Blaming employees for systemic problems is easy. Designing a workflow that prevents those problems is harder.

But the latter is what actually improves operations.

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The great healthcare technology paradox: More software, more administrative work?

Healthcare has invested heavily in electronic health records, practice management systems, clearinghouses, billing platforms, and specialized revenue cycle management services.

Yet physicians continue to complain about administrative complexity.

How is that possible?

We have digitized the paperwork, but have we actually simplified the work?

Consider a typical independent practice.

One system manages scheduling. Another maintains clinical documentation. A third handles claims. A separate portal checks eligibility. Another website manages authorization requests.

The employees become the integration layer.

They copy information, verify information, reconcile information, and occasionally discover that two systems disagree about the same patient.

That is an expensive way to connect software.

It is also a curious definition of automation.

If your employee has to move information between five systems, your practice may have automated the software but not the workflow.

The industry often measures technological progress by the number of functions a product offers.

Physicians should ask a different question:

How many administrative steps can this technology eliminate without creating another problem?

A dashboard with 25 features is not necessarily better than a system that prevents one recurring source of errors.

More technology is not the same as better technology.

And more automation is not necessarily more efficiency.

Sometimes the most sophisticated solution is to stop making people do unnecessary work.

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The AI debate: Should we train people better or replace the process with machines?

The original debate presents two competing philosophies.

One side argues that human discipline and structured workflows are the foundation of reliable medical billing.

The other argues that payer complexity has become too extensive for humans to manage without adaptive AI.

Both arguments have merit.

Both also miss something important.

The human accountability argument

The traditional approach begins with a simple principle:

Verify before you bill.

Patient demographics should be accurate. Insurance coverage should be checked. Authorization requirements should be understood. Clinical documentation should support the services provided.

These are foundational responsibilities.

AI should not become an excuse for careless registration or incomplete documentation.

But expecting a front-desk employee to memorize every payer rule, benefit limitation, authorization requirement, and coding update is unrealistic.

A receptionist is not a walking payer-policy database.

And if the practice's entire revenue cycle depends on one experienced employee remembering everything, that is not a robust system.

That is an operational vulnerability wearing a name badge.

The autonomous AI argument

The technology-centered position makes another valid observation.

Payer requirements change. Documentation standards evolve. Different insurance plans have different rules. Staff turnover creates gaps in institutional knowledge.

AI can help monitor information, identify inconsistencies, and reduce repetitive administrative tasks.

It can flag a missing authorization. It can compare an eligibility response against available patient information. It can identify recurring rejection patterns.

It can also help organize information before the claim reaches the clearinghouse.

But there is a problem with the idea of completely autonomous billing.

AI cannot responsibly invent information simply because a required field is empty.

A missing diagnosis is not an invitation to create one.

A coding inconsistency is not permission to change the clinical record.

A medical necessity denial is not proof that the physician failed to provide appropriate care.

The system must distinguish between what it knows, what it can infer, and what requires human judgment.

The third position: Human-supervised, AI-powered operations

I believe the more useful debate is not human versus AI.

It is whether we can design systems that allow humans and AI to perform the work each is best equipped to handle.

Humans contribute clinical judgment, accountability, contextual understanding, and exception handling.

AI contributes speed, pattern recognition, validation, consistency, and administrative assistance.

Structured information connects the two.

The goal should be:

Capture → Structure → Preserve → Validate → Propagate → Act → Learn.

Instead of waiting for a claim to fail, the system evaluates whether the necessary information is available, consistent, and current before the next step.

That is a fundamentally different way to think about revenue cycle management.

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The numbers: Healthcare's administrative headache is not imaginary

Let's look at what the latest industry research tells us.

Experian Health's 2026 State of Claims survey, released October 28, included 204 healthcare professionals involved in finance, billing, registration, reimbursement, claims, and collections.

The findings are revealing.

Claim denial statistics

• 40% reported that at least 10% of their claims were denied.
• 75% said claim denials were increasing.
• 78% said payer policies were changing more frequently.
• 79% said obtaining reimbursement had become more challenging.
• 64% said reimbursement timelines were increasing.
• 89% said reducing denials was a priority in 2026.

The most frequently cited denial reasons included:

• 53%: Services not covered.
• 31%: Frequently changing payer policies.
• 25%: Coding inaccuracy.
• 25%: Difficulty obtaining prior authorization.

These percentages represent the share of respondents identifying reasons among their leading denial causes, not the percentage of all claims denied for each reason.

There is another finding worth considering.

Only 30% of respondents reported recovering between 80% and 100% of denied revenue.

In other words, correcting the claim does not necessarily mean recovering the money.

That is an important distinction for independent practices.

A large health system may have a dedicated denial management department. A small practice may have one or two employees handling billing, collections, insurance calls, and everything else that happens before lunch.

The same denial can have very different operational consequences.

The automation statistics

An October 20 MGMA poll of 214 respondents found that:

• 63% were moving toward more revenue cycle automation.
• 26% expected no major change.
• 11% were moving toward more manual operations.

The direction is clear.

Healthcare organizations want more automation.

But wanting automation and implementing useful automation are two different things.

The uncomfortable conclusion

We are seeing substantial investment in technology while reimbursement complexity remains a major operational challenge.

Perhaps the question should no longer be:

How do we process claims faster?

Perhaps it should be:

Why are we still spending so much time correcting information that should have been reliable before the claim was created?

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Recent news: Three developments that deserve a closer look

The healthcare technology conversation is changing quickly. Three October 2026 developments illustrate why.

Development one: Denials are changing, not disappearing

On October 30, Medical Economics reported that services not covered had become the leading denial concern in Experian Health's 2026 survey.

This is interesting because it challenges the assumption that most billing problems are simply registration mistakes.

Patient demographic accuracy remains important, but payer coverage policies and changing requirements are also major contributors.

A practice can submit a technically clean claim and still encounter a coverage problem.

The lesson: Clean claims are necessary, but they are not the same as payable claims.

Development two: Medical groups are moving toward automation

MGMA reported on October 21 that 63% of surveyed medical groups were moving toward more revenue cycle automation.

Physicians Practice also reported on October 27 that coding, prior authorization, denials, and payment posting were leading areas of interest.

Julia Rosen, senior vice president of IT at MGMA, emphasized the importance of examining existing EHR capabilities before adding more standalone AI tools.

That is sensible advice.

Before buying another platform, ask whether your current systems already provide the functionality you need.

Otherwise, your practice may end up with another subscription and another password.

Development three: Is healthcare AI making the system more expensive?

On October 14, Becker's Hospital Review reported on a Peterson Health Technology Institute analysis questioning whether administrative AI tools are actually reducing healthcare costs.

The report argued that AI applications involving documentation, coding, billing, and prior authorization could reinforce existing fee-for-service incentives and increase administrative activity.

Caroline Pearson, executive director of the institute, warned:

"On our current trajectory, AI is set to exacerbate existing system failures."

That deserves attention.

The objective of healthcare AI should not be to make inefficient processes run faster.

If an inefficient workflow takes 20 steps, automating all 20 steps may simply give us a faster inefficient workflow.

The real innovation is eliminating unnecessary steps.

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Three expert perspectives: What should physicians take away?

The following perspectives come from healthcare leaders and researchers discussing the changing economics and operations of healthcare technology.

Expert one: Shawntea "Taya" Gordon, MBA, FACMPE

CEO, Atlas & Perpetua Healthcare Consulting

Gordon has highlighted the growing imbalance between automated payer reviews and largely manual provider appeals.

Her observation is particularly relevant to independent practices.

Payers can use technology to examine claims at scale. Provider organizations may still be relying on manual processes to understand and challenge those decisions.

The practical lesson:

Practices need better visibility into denials, faster identification of recurring problems, and a more systematic approach to appeals.

My interpretation: If the payer can process a denial in seconds but the physician's office needs several days to understand it, the technology imbalance is already part of the business problem.

Expert two: Julia Rosen

Senior Vice President of IT, Medical Group Management Association

Rosen's advice is to examine the capabilities already available in existing EHR systems before introducing additional standalone AI products.

This is especially important for smaller practices.

A technology purchase should solve a documented problem, not simply respond to an impressive demonstration.

My interpretation: The first question should not be, "Which AI product should I buy?" It should be, "Where does my current workflow break?"

Expert three: Caroline Pearson

Executive Director, Peterson Health Technology Institute

Pearson has challenged the assumption that administrative AI automatically creates healthcare savings.

Her concern is that existing payment incentives can encourage more activity rather than better outcomes.

That is a crucial distinction.

An AI system can generate more documentation, more coding activity, and more automated interactions without reducing the underlying cost of care.

My interpretation: If AI increases the number of administrative transactions but does not reduce the work required to deliver and collect payment for care, what exactly have we improved?

Three experts. Three different concerns. One common lesson.

Gordon highlights the imbalance.

Rosen emphasizes practical integration.

Pearson questions the economics.

Together, they point toward a more disciplined approach to healthcare technology.

Do not automate blindly. Do not buy software impulsively. Measure what actually improves.

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The rejection-versus-denial problem: Stop treating two different problems as one

Medical billing has its own vocabulary.

Unfortunately, the vocabulary is not always used consistently.

A claim rejection and a claim denial are not the same thing.

Rejection: The claim did not get through the front door

A rejection generally occurs before payer adjudication.

A clearinghouse or payer front-end system identifies a problem that prevents normal processing.

Examples include:

• Missing information.
• Invalid patient identifiers.
• Incorrect provider identifiers.
• Invalid data formats.
• Inconsistent claim elements.

The 277CA transaction can communicate claim acknowledgment and front-end status information.

Think of it as the claim's initial checkpoint.

Denial: The payer reviewed the claim and refused payment

A denial occurs after payer adjudication.

Possible reasons include:

• Coverage exclusions.
• Missing authorization.
• Medical necessity requirements.
• Coding problems.
• Timely filing.
• Benefit limitations.
• Coordination-of-benefits issues.

The 835 Electronic Remittance Advice communicates payment and adjustment information.

The distinction matters because the solutions differ.

A demographic error needs correction.

A coverage issue needs plan-specific investigation.

A medical necessity issue requires examination of the clinical documentation and applicable coverage criteria.

A rejection asks whether the claim can proceed. A denial raises the question of whether the payer will pay.

Confusing the two is like taking your car to a mechanic because it failed an emissions test when the actual problem is that it has no engine oil.

Both are problems. They are not the same problem.

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Seven practical ways to stop feeding the denial machine

Here is where the discussion becomes useful.

A small practice does not need a million-dollar technology transformation to begin improving its revenue cycle.

It needs a better understanding of where the work is breaking.

1. Stop treating patient registration as data entry

Registration is the beginning of the financial information chain.

Validate:

• Legal name.
• Date of birth.
• Insurance identification.
• Subscriber information.
• Relationship to subscriber.
• Contact information.
• Coordination-of-benefits information.

Do not assume yesterday's information is still accurate today.

Practical action: Establish a consistent demographic and insurance verification process.

2. Verify eligibility, but understand its limitations

The X12 270/271 transactions support electronic eligibility inquiries and responses.

However, eligibility is not a guarantee of payment.

Coverage, benefits, authorization, network status, and medical necessity can still affect reimbursement.

Practical action: Preserve the eligibility response, verification date, and unresolved coverage questions.

3. Stop managing authorization through memory

An authorization should not exist only in someone's email or on a sticky note.

Track:

• Whether authorization is required.
• Payer and plan.
• Requested service.
• Submission date.
• Supporting documentation.
• Approval status.
• Authorization number.
• Effective and expiration dates.
• Approved services and limits.

Practical action: Maintain a centralized authorization record connected to the patient and encounter.

4. Do not let AI manufacture clinical documentation

AI can identify missing information and suggest areas requiring review.

It cannot legitimately create a diagnosis simply because the claim needs one.

Practical action: Require documentation-supported coding and human review of material clinical inconsistencies.

5. Read the rejection before resubmitting the claim

Repeatedly resubmitting the same incorrect claim is not a strategy.

It is repetition with optimism.

Practical action: Categorize rejection reasons by payer, provider, service, source, and corrective action.

6. Use denial information as operational intelligence

A recurring authorization denial may reveal a scheduling problem.

A recurring coverage denial may indicate that payer policies have changed.

A documentation-related denial may indicate a communication gap between clinical and administrative workflows.

Practical action: Review patterns, not just individual claims.

7. Measure the cost of administrative friction

Track:

• First-pass acceptance.
• First-pass payment.
• Rejection rate.
• Denial rate.
• Days in accounts receivable.
• Denial recovery.
• Cost per claim.
• Staff touches per claim.
• Authorization turnaround time.
• Repeat denial rate.

One particularly revealing metric is staff touches per claim.

If AI processes more claims but employees still need to intervene just as often, the practice may not have achieved meaningful automation.

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A step-by-step roadmap for modernizing your practice

Do not start with the software.

Start with the workflow.

Step 1: Map the actual process.

Follow the patient from registration to final payment.

Identify every point where information is entered, transferred, checked, or corrected.

Step 2: Identify the recurring failures.

Review recent rejections, denials, and delayed payments.

Group them by reason.

Step 3: Find the original source.

Ask where the information originated, who entered it, whether it was verified, and whether it remained accurate.

Step 4: Standardize the information.

Define consistent fields, validation rules, and ownership responsibilities.

Step 5: Automate repetitive work.

Begin with missing-field detection, eligibility review, authorization tracking, claim completeness, and denial categorization.

Step 6: Establish human escalation.

Define which actions AI can perform and which require human approval.

Step 7: Measure the result.

Compare the new workflow against your original baseline.

If the technology creates more work than it eliminates, reconsider the implementation.

The objective is not to demonstrate that the practice uses AI.

It is to demonstrate that the practice operates better because of it.

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Pitfalls: Seven ways to make an expensive problem even more expensive

Pitfall one: Automating a broken workflow.

Garbage in, garbage out. AI simply gives the garbage better transportation.

Pitfall two: Blaming the billing department.

The billing team may be discovering errors created elsewhere.

Pitfall three: Assuming eligibility means payment.

Coverage verification does not guarantee reimbursement for a particular service.

Pitfall four: Buying disconnected AI tools.

Another subscription does not automatically improve integration.

Pitfall five: Giving AI unrestricted coding authority.

Clinical documentation must reflect actual care, not an algorithm's preferred reimbursement outcome.

Pitfall six: Ignoring vendor dependency.

A consolidated platform may simplify operations, but continuity planning remains essential.

Pitfall seven: Measuring software activity instead of business outcomes.

A thousand automated actions mean very little if the practice's cash flow and administrative burden remain unchanged.

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Myth busters: What the industry needs to stop believing

Myth: AI will eliminate medical billers.

Reality: AI can reduce repetitive work, but human expertise remains essential for exceptions, compliance, and complex payer interactions.

Myth: Clean claims always get paid.

Reality: A technically valid claim can still fail because of coverage, authorization, medical necessity, or contractual requirements.

Myth: Every denial is a billing error.

Reality: The problem may originate in registration, scheduling, clinical documentation, payer policy, or system integration.

Myth: More AI means greater efficiency.

Reality: Poorly designed automation can accelerate existing inefficiencies.

Myth: AI can independently establish medical necessity.

Reality: AI can assist with information review, but clinical judgment and applicable coverage criteria remain essential.

Myth: One integrated platform eliminates all risk.

Reality: Consolidation can reduce integration friction while increasing vendor dependency.

Myth: A denial is simply lost revenue.

Reality: It can also be a signal of a recurring operational problem.

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Legal and regulatory implications: AI does not get a compliance exemption

Healthcare technology operates within a complicated legal environment.

A practice cannot outsource its responsibility simply because an algorithm performed the task.

HIPAA and patient information

The HIPAA Privacy, Security, and Breach Notification Rules establish obligations involving protected health information.

Practices should evaluate:

• Business associate agreements where required.
• Access controls.
• Encryption.
• Audit trails.
• Data retention.
• Incident response.
• Subcontractor access.
• Secure information exchange.

Do not assume that every consumer AI platform is appropriate for handling PHI.

Billing and coding integrity

Claims must be supported by accurate documentation and applicable coding rules.

AI suggestions should not become unsupported billing entries.

The physician's record must accurately represent the care provided.

False Claims Act

Knowingly submitting false or fraudulent claims to federal healthcare programs can create serious liability under the federal False Claims Act.

AI does not eliminate the responsibilities of the provider or organization submitting the claim.

CMS interoperability and prior authorization

CMS-0057-F, the 2024 Interoperability and Prior Authorization Final Rule, established requirements for specified payers.

Many operational provisions began in 2026, while major API implementation requirements generally begin in 2027, subject to the rule's specific timelines and exceptions.

The rule is important because it moves the industry toward more standardized information exchange.

But interoperability is not merely about moving data.

The data must retain its meaning, accuracy, and context.

Practices should also monitor applicable state laws, payer contracts, Medicare requirements, and other regulatory developments.

Obtain qualified legal and compliance advice before deploying systems that influence clinical documentation, coding, reimbursement, or coverage decisions.

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Ethical considerations: Getting paid is important. Getting it right is more important.

There is a temptation to measure billing technology by one number: reimbursement.

That is insufficient.

The system must also protect clinical integrity.

An AI system should not invent a diagnosis to improve payment.

It should not alter the medical record without appropriate authorization and documentation.

It should not encourage unnecessary services or unsupported coding.

Patients should not experience delays because disconnected systems failed to communicate.

And physicians should know when a machine is making a recommendation, what information supports it, and who is responsible for approving an action.

The ethical objective is simple:

Accurate care. Accurate documentation. Appropriate reimbursement.

Anything else risks turning automation into another source of healthcare dysfunction.

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Tools, metrics, and resources: What should a small practice actually use?

A small practice does not need every available technology.

It needs a coherent operational environment.

Consider these categories:

Electronic health records: Clinical documentation, encounters, orders, and patient information.

Practice management systems: Scheduling, registration, billing, and operational workflows.

Eligibility tools: Electronic coverage inquiries and payer responses.

Clearinghouses: Claim submission, acknowledgments, and front-end validation.

ERA processing: Payment and adjustment information from 835 transactions.

Authorization management: Requirements, submissions, approvals, expiration dates, and supporting records.

Analytics: Rejection trends, denial patterns, accounts receivable, and operational performance.

AI-assisted validation: Missing information, inconsistencies, repetitive patterns, and workflow recommendations.

Five questions to ask a technology vendor

1. Does the product integrate with our existing systems?
2. Can it explain why it flagged a claim or patient record?
3. Does it preserve the original source information?
4. Can we audit AI-generated recommendations and human approvals?
5. Can you demonstrate measurable reductions in administrative work?

And one more:

If we stop using your product tomorrow, can we retrieve our data and continue operating?

That question may be more important than the product demonstration.

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The OnnX perspective: Stop treating the claim as the starting point

This is where the discussion becomes particularly important to me as a physician and entrepreneur.

At OnnX, the thesis is that healthcare billing is a data quality and context problem, not simply a tooling problem.

The industry often focuses on the claim because that is where the financial consequences become visible.

But the claim is not where the story begins.

The information begins much earlier.

A patient has a demographic record.

That patient has insurance coverage.

The coverage has eligibility conditions, benefits, limitations, and possibly authorization requirements.

The patient has an encounter.

The encounter has clinical documentation.

The documentation supports coding.

The coding contributes to the claim.

The claim enters the payer's adjudication process.

These are not unrelated pieces of information.

They are connected parts of one operational story.

Yet many systems treat them as separate transactions.

That is the opportunity.

What OnnX is exploring

OnnX is an AI-powered medical billing SaaS concept for small and medium-sized physician-owned practices.

The goal is to reduce administrative friction and help practices manage their revenue cycle with greater visibility and control.

The philosophy is human-supervised and AI-powered.

Not AI replacing physicians.

Not AI inventing clinical justification.

Not another software layer that requires employees to reconcile five different dashboards.

The focus is on structuring and validating information before it becomes a downstream problem.

The operating concept:

Capture: Collect relevant patient, coverage, and encounter information.

Structure: Organize it consistently so it can be interpreted and used.

Preserve: Maintain the context as information moves through the workflow.

Validate: Identify missing, inconsistent, or outdated information.

Propagate: Make relevant information available to the appropriate connected workflows.

Act: Support appropriate administrative actions and escalate uncertain decisions.

Learn: Identify recurring patterns and improve the process.

Consider prior authorization

A traditional reactive process discovers that authorization is missing after the claim has been submitted or denied.

A more proactive process identifies the requirement before the service, tracks the request, records the approval, and connects the evidence to the encounter.

The first process manages a failure.

The second attempts to prevent one.

That is the difference between reactive revenue cycle management and preventive revenue cycle management.

Why independent practices?

Independent practices have little room for unnecessary administrative complexity.

They often operate with smaller teams, tighter margins, limited IT resources, and less capacity to absorb reimbursement delays.

They need technology that is practical and economically defensible.

They do not need another dashboard simply because the dashboard has artificial intelligence in its product description.

The objective is to reduce unnecessary work, improve information continuity, and help physicians preserve their independence.

The physician should be thinking about the patient, not wondering which software system lost the authorization.

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Future outlook: The next competitive advantage may be what you prevent

Healthcare AI will continue to evolve.

But I believe several developments will determine which technologies create lasting value.

More intelligent eligibility and authorization

Payer interoperability initiatives may make relevant information more accessible through standardized APIs.

The challenge will be integrating that information into actual practice workflows.

AI-assisted denial prevention

Systems will increasingly analyze rejection and remittance patterns.

The most useful platforms will not simply tell practices what went wrong.

They will help identify where the problem originated and how to prevent recurrence.

Structured information and context preservation

Moving data between systems is not enough.

Systems need to preserve the relationships between patient, coverage, authorization, encounter, documentation, and claim.

Human-supervised AI agents

AI agents will likely take on more repetitive administrative activities.

Their authority should remain bounded by defined rules, audit trails, and escalation requirements.

Greater regulatory scrutiny

Payers, providers, regulators, and patients will increasingly examine how AI affects clinical judgment, coding, medical necessity, and reimbursement.

Measurable return on investment

Healthcare practices will become less impressed by AI demonstrations and more interested in measurable results.

The important questions will be:

• How many errors were prevented?
• How many staff hours were saved?
• How much unnecessary rework disappeared?
• Did first-pass payment improve?
• Did days in accounts receivable decrease?
• Was clinical accuracy preserved?

The future will not belong to the company with the most AI features.

It will belong to the companies that solve meaningful problems without creating new ones.

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Frequently asked questions

1. What is the biggest problem in medical billing?

There is no single cause. Problems can originate in inaccurate registration, coverage limitations, authorization, coding, documentation, payer policy, and disconnected systems. Many are information and workflow problems rather than isolated billing mistakes.

2. Can AI eliminate claim denials?

No. AI can help prevent avoidable errors, identify patterns, and improve workflows, but it cannot eliminate payer coverage limitations, contractual disputes, or every documentation and medical necessity issue.

3. What is the difference between a rejection and a denial?

A rejection generally occurs before payer adjudication because the claim fails front-end processing. A denial occurs after the payer adjudicates the claim and determines that payment will not be made or will be reduced.

4. Can AI replace medical billers?

AI can automate repetitive tasks, but human expertise remains important for exceptions, appeals, compliance, and complex payer interactions.

5. Does eligibility verification guarantee payment?

No. It provides information about coverage and benefits. It does not guarantee that a particular service is covered, authorized, medically necessary, or payable.

6. Why should practices focus on upstream data quality?

Because inaccurate or incomplete information introduced early can create multiple downstream problems, increasing administrative work and delaying reimbursement.

7. What should a practice measure when adopting AI?

Rejection rate, denial rate, first-pass payment, days in accounts receivable, denial recovery, cost per claim, and staff touches per claim are useful measures.

8. Is one integrated billing platform always better?

No. Integration can reduce information-transfer problems, but practices must also consider vendor dependency, security, reliability, portability, and contingency planning.

9. Can AI change clinical codes to obtain payment?

AI should not create unsupported diagnoses or modify documentation to secure reimbursement. Coding must accurately reflect the services provided and applicable rules, with appropriate human review.

10. What is preventive revenue cycle management?

It is an approach that identifies and addresses information gaps before they become claim rejections, denials, delayed payments, or costly administrative corrections.

11. What is the most important question to ask an AI billing vendor?

Ask what measurable operational problem the product eliminates and how the vendor proves that the improvement is real.

12. What is OnnX trying to accomplish?

OnnX is exploring AI-powered medical billing technology that helps independent physician practices structure, validate, and manage billing-related information with human supervision and greater workflow continuity.

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Final thoughts: Stop celebrating the repair. Start questioning the failure.

There is something strange about an industry that spends so much money correcting information after it has already failed.

We have built sophisticated systems to process claims, analyze denials, automate appeals, and move administrative work faster.

But speed is not the same as progress.

If a practice repeatedly makes the same registration error, faster denial management is not the answer.

If authorization information disappears between systems, another dashboard is not the answer.

If clinical documentation and billing data become disconnected, an AI agent that guesses what is missing is certainly not the answer.

The best denial is the one that never happens.

The best administrative task is the one the physician's office no longer has to perform.

And the best AI is not necessarily the AI that does the most work. It may be the AI that makes unnecessary work disappear.

Healthcare needs to move beyond the question of how to process claims more efficiently.

It needs to ask how to create better information, preserve context, and prevent avoidable failures before they reach the billing department.

That is a much more interesting problem.

And perhaps a much more important one.

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Get involved: Help change the conversation

Let me ask a question that may be uncomfortable for the healthcare technology industry:

Are we actually solving medical billing problems, or are we becoming increasingly sophisticated at managing the consequences of bad information?

I would like to hear from physicians, independent clinic owners, medical billers, and practice administrators.

What is the one administrative problem in your practice that you believe should have been eliminated years ago?

Share your experience in the comments.

If this perspective challenges how you think about medical billing, consider reposting it. Someone in your professional network may be dealing with exactly the same problem.

The future of healthcare technology should be shaped by the people who understand the work.

Question the process. Challenge the assumptions. Help build something better.

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About the Author

Dr. Daniel Cham is a physician-entrepreneur and medical consultant with experience in healthcare management, medical practice operations, and medical billing.

He is the founder of OnnX, an emerging AI-powered medical billing SaaS initiative focused on helping small and medium-sized physician-owned practices reduce administrative friction through structured information, intelligent validation, and human-supervised automation.

His interests include healthcare technology, artificial intelligence, revenue cycle management, and the future of independent medicine.

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Disclaimer

This article is intended for general educational and informational purposes only. It should not be interpreted as medical, legal, financial, coding, or regulatory advice. Requirements may vary by payer, jurisdiction, and individual circumstances. Readers should consult appropriately qualified professionals before making operational, clinical, or compliance decisions.

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Continue the Conversation

Healthcare is changing. The important question is whether our systems are changing in ways that actually help the people delivering care.

Explore more perspectives, practical ideas, and discussions about healthcare operations, medical technology, and innovation.

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Knowledge drives progress. Keep questioning conventional thinking, explore new possibilities, and contribute to a better future for healthcare.

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If you found this article useful, consider reposting it to help other physicians and clinic owners rethink the relationship between medical billing, information quality, and administrative complexity.

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References and Further Reading

Industry Research, Reports, and Expert Perspectives

1. Shryock, T. (October 30, 2026). Why are claims being denied in 2026? 'Services not covered' now tops the list. Medical Economics.
   Examines the 2026 State of Claims survey, including denial trends, payer policy changes, reimbursement challenges, and the growing gap between denied claims and recovered revenue.
2. Experian Health. (October 28, 2026). Healthcare claim denial statistics: State of Claims Report 2026.
   Provides the underlying survey findings on denial rates, common denial reasons, payer reimbursement challenges, and provider interest in AI and automation. The survey included 204 healthcare professionals involved in financial, billing, registration, reimbursement, claims, and collections decisions.
3. MGMA Revenue Cycle Insights. (October 21, 2026). Most medical groups adding RCM automation amid a wave of payer-driven demands. Medical Group Management Association.
   Reports that 63% of surveyed medical group leaders were leaning toward more revenue-cycle automation in 2026. Also examines coding, prior authorization, denials, appeals, and payment posting as automation priorities.
4. The Hidden Cost of Medical Billing: Why Clinics Are Losing Revenue in Silence (and How AI Is Reshaping the Middle Layer of Healthcare)
   Explores how fragmented workflows, documentation ambiguity, and downstream corrections create administrative costs and revenue leakage.
5. The World Cup Has Better Real-Time Visibility Than Your Medical Billing System — And That's the Problem
   Examines visibility latency, delayed feedback, and why practices often discover revenue-cycle problems too late.

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