Daniel Cham MD: 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?"

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:

Registration.
Insurance verification.
Scheduling.
Prior authorization.
Patient encounter.
Clinical documentation.
Coding.
Charge capture.
Claim preparation.
Claim submission.
Payer adjudication.
Payment posting.
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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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:

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

References & Further Reading

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.
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.
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.

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.
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.
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.

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

Daniel Cham MD

Saturday, October 31, 2026

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

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