Daniel Cham MD

Monday, November 2, 2026

Your Billing Department Is Fighting Yesterday's Mistakes. Who Is Preventing Tomorrow's?

The $40 Problem Nobody Wants to Fix: Why Medical Billing Denials Often Begin Before the Claim Even Exists

“The greatest obstacle to discovery is not ignorance—it is the illusion of knowledge.” — Daniel J. Boorstin.

Everyone Is Fixing the Claim. Nobody Is Fixing the Problem.

Imagine this.

A physician in New York finishes a full day of patient appointments.

The waiting room was full. The clinical team was busy. Patients received care. Charts were completed. Everyone is exhausted.

Then the billing department discovers that several claims have been rejected or denied.

One patient had an incorrect insurance number. Another had an expired policy. A third needed prior authorization. Someone entered the wrong demographic information. Another claim is missing supporting documentation.

What happens next?

The billing team starts investigating.

Someone opens the clearinghouse portal. Someone else calls the payer. Another employee checks the patient's insurance. Someone corrects the claim and submits it again.

The practice is now paying people to repair mistakes that may have occurred days or weeks earlier.

And here is the funny part.

When the money does not arrive, everyone looks at billing.

Apparently, the billing department is responsible for everything that happened before the claim reached its desk.

That is like blaming the plumber because somebody forgot to turn off the faucet.

We have become very good at fixing claims.

But how good are we at preventing the problems that create them?

This is the uncomfortable question physician-owned practices need to ask.

Because sometimes the billing department is not the source of the problem.

It is simply the last department forced to deal with it.

The First Mistake: Treating Rejections and Denials Like the Same Disease

Let's start with something surprisingly simple.

A rejected claim and a denied claim are not interchangeable.

Yet practices sometimes lump them together under one convenient heading: unpaid claims.

That is like treating a flat tire and an engine failure as the same automotive problem.

Both stop the car. The repair is different.

Clearinghouse rejections happen before payer adjudication.

The claim has not successfully made it through the clearinghouse's applicable submission requirements.

Common causes include:

Invalid provider identifiers.
Incorrect patient demographics.
Missing information.
Invalid insurance identification numbers.
Incorrect payer routing.
Technical formatting problems.

Think of a rejection as an email that never reached its destination because someone typed the wrong address.

The message was never properly delivered.

Payer denials happen after the insurance company receives and adjudicates the claim.

Common reasons include:

Medical necessity disputes.
Noncovered services.
Missing prior authorization.
Inadequate documentation.
Coding or modifier problems.
Timely filing issues.
Coordination-of-benefits problems.

A denial is closer to receiving a response that says, "We received your request. We are not paying it."

One requires correction of a submission problem.

The other may require investigation, documentation, reconsideration, or an appeal.

And sometimes the payer is wrong.

Sometimes the provider is wrong.

Sometimes both sides are looking at different information.

The lesson: Stop treating every unpaid claim as one problem. Diagnose the problem before prescribing the solution.

The Front Desk: The Most Underestimated Department in Revenue Cycle Management

Here is a contrarian thought.

What if the most important revenue-cycle employee is not the biller?

What if it is the person who registers the patient?

Consider what happens before a physician sees a patient.

A patient schedules an appointment.

Insurance information is collected.

Eligibility is checked.

Benefits are reviewed.

Authorization requirements are identified.

Patient demographics are entered.

Every one of these steps creates information that may affect reimbursement later.

Now imagine one small mistake.

The insurance policy is inactive.

The patient has changed insurance providers.

The authorization was never requested.

The wrong payer was selected.

The information was entered incorrectly.

The physician provides the service.

The biller submits the claim.

The payer rejects or denies it.

And suddenly, everyone is discussing coding.

The coding may be perfectly correct. The problem may have existed before the physician entered the room.

This is why front-end revenue cycle management deserves more attention.

The front desk is not simply an administrative checkpoint.

It is an important source of financial information.

A practice can have excellent physicians, experienced billers, sophisticated software, and still struggle because the information entering the system is incomplete.

A beautiful billing dashboard cannot rescue information that was never captured correctly.

The Numbers: Revenue Leakage Is Not a Small Administrative Annoyance

Let's examine the industry evidence.

The numbers are important because denial management is frequently discussed as a billing issue rather than an operational and financial sustainability issue.

The 2026 State of Claims Findings

Experian Health's 2026 State of Claims survey, released October 28, 2026, reported:

75% of surveyed healthcare professionals said claim denials were increasing.
40% reported denial rates of at least 10%.
89% identified denial reduction as an organizational priority.
65% said submitting clean claims had become more difficult.
64% reported increasing reimbursement timelines.
78% said payer policies were changing more frequently.
84% believed AI could improve claims management in some capacity.

One particularly important finding was that services not covered were the most frequently cited denial reason among the surveyed organizations.

These findings suggest that healthcare organizations are facing a more complicated reimbursement environment.

The survey covers healthcare organizations broadly, so its findings should not automatically be interpreted as the specific experience of small independent practices.

The Prior Authorization Burden

The American Medical Association has documented the substantial administrative burden associated with prior authorization.

Its 2026 survey reported:

Physicians complete approximately 40 prior authorizations per week.
These activities consume approximately 13 hours of physician and staff time weekly.
94% of surveyed physicians reported that prior authorization contributes to burnout.
74% reported an increase in denials over the previous five years.

Think about that.

Thirteen hours.

That is not a minor administrative inconvenience.

That is time that could otherwise be spent supporting patients, managing a practice, or addressing clinical responsibilities.

We have somehow built a healthcare environment where physicians need to spend hours proving that they are allowed to provide care.

And we wonder why everyone is exhausted.

The Front-End Connection

Industry research has repeatedly identified registration, eligibility verification, and authorization as important contributors to claim problems.

The exact proportion of preventable denials varies by study, payer, specialty, and methodology.

So let's stop repeating impressive statistics without asking where they came from.

A practice should measure its own preventable denial rate.

The number that matters most is not how many claims were denied. It is how many could reasonably have been prevented.

Three Expert Perspectives: What the Industry Is Telling Us

Rather than inventing quotations, this section draws on three identifiable industry perspectives and their published findings.

Expert 1: Mindy Fortson — Experian Health

The issue: Growing denial pressure and the need for better claims management.

Experian Health's 2026 findings highlight the increasing difficulty healthcare organizations face in maintaining clean claims and managing payer reimbursement requirements.

The underlying message is that denial management needs to become more systematic.

A practice should not simply count denials.

It should understand their causes.

Practical lessons:

Track recurring denial categories.
Improve patient registration accuracy.
Identify payer-specific problems.
Evaluate AI based on measurable operational improvement.
Reduce unnecessary manual intervention.

My interpretation: If your team sees the same denial every week, you do not have five separate problems. You may have one broken process repeating itself.

Expert 2: The American Medical Association — Physician Advocacy

The issue: Administrative burden and prior authorization.

The AMA's findings illustrate how payer requirements consume physician and staff time.

The problem extends beyond reimbursement.

It affects clinical workflow, patient access, and physician well-being.

Practical lessons:

Centralize authorization tracking.
Identify frequently authorized services.
Verify requirements before appointments and procedures.
Preserve supporting documentation.
Track delays and repeated requests.
Escalate recurring payer problems.

My interpretation: Physicians should not need three browser tabs, two spreadsheets, and a telephone call just to determine whether an administrative requirement has been completed.

Expert 3: Wael Khouli, MD, MBA — Healthcare Revenue Cycle Commentary

The issue: Changing payer requirements and reimbursement complexity.

Recent healthcare revenue-cycle coverage has highlighted the challenges created by changing payer policies and increasingly complex coverage requirements.

A service paid last month may encounter different requirements today.

That means practices cannot rely entirely on historical experience.

Practical lessons:

Monitor payer policy changes.
Maintain payer-specific workflow references.
Communicate relevant changes to staff.
Review denial trends by payer and service.
Document medical necessity accurately.

My interpretation: Experience is valuable. But experience without updated information can become an expensive habit.

These perspectives point toward one conclusion.

Revenue cycle management needs to move beyond correcting individual claims and toward improving the processes that produce them.

Recent News: The Industry Is Finally Having the Right Conversation

Three developments during the week of October 26–November 2, 2026, reinforce the importance of upstream revenue-cycle improvement.

October 28: Experian Health's 2026 State of Claims findings

The survey highlights growing denial pressure, changing payer policies, and increasing interest in AI-assisted claims management.

Read the Experian Health announcement

October 28: Becker's Hospital Review examines denial causes

The coverage discusses the continuing problems associated with claim denials, coverage requirements, coding, and payer policies.

Read the Becker's analysis

October 30: Medical Economics examines uncovered services

The report discusses the growing importance of coverage restrictions and the financial consequences of denied claims.

Read Medical Economics

The bigger message?

The industry is increasingly recognizing that claim problems are connected to information, coverage, administrative processes, and payer complexity.

But recognition is only the beginning.

The real question is whether practices are changing how they operate.

Eight Places Where Your Revenue Cycle May Be Breaking

Let's follow the patient journey.

1. Scheduling

Was the correct patient information collected?

Were insurance changes identified?

2. Registration

Were demographics entered accurately?

Was the correct insurance plan selected?

3. Eligibility verification

Was coverage active?

Were applicable benefits and exclusions reviewed?

4. Prior authorization

Was authorization required?

Was it obtained for the correct service, provider, and date range?

5. Clinical documentation

Does the medical record accurately support the services provided?

6. Coding

Do the codes accurately reflect the documented encounter?

Are applicable modifiers correct?

7. Claim submission

Did the claim pass required technical and administrative edits?

8. Payment and reconciliation

Was the claim paid correctly?

If not, was the actual reason identified?

Notice something?

The billing department is involved in only part of this journey.

Yet it often inherits the consequences of every preceding stage.

The revenue cycle is a relay race. If one person drops the baton, the last runner cannot magically win the race.

Ten Practical Steps to Stop Repeating the Same Billing Mistakes

Here is where theory meets operations.

Small practices do not need a multimillion-dollar technology investment to start improving their revenue cycle.

They need visibility, accountability, and repeatable processes.

Step 1: Establish your baseline.

Review 90 days of claims.

Measure rejection rates, denial rates, first-pass resolution, days in accounts receivable, and recovered revenue.

Step 2: Separate rejections from denials.

Do not combine different problems into one metric.

Step 3: Categorize recurring errors.

Create categories for eligibility, demographics, authorization, coding, documentation, coverage, and timely filing.

Step 4: Standardize registration.

Establish a consistent process for collecting and validating patient information.

Step 5: Verify insurance before appointments.

Check coverage and relevant benefits at an appropriate interval before the service.

Step 6: Track authorization centrally.

Stop relying on memory and scattered email conversations.

Step 7: Improve clinical and administrative communication.

Make sure missing documentation and authorization issues are identified early.

Step 8: Use claim-scrubbing tools.

Identify technical errors before submission.

Step 9: Perform root cause analysis.

Ask why the error occurred, not simply how to correct it.

Step 10: Review performance weekly.

Assign responsibility for corrective actions and measure whether recurring problems decrease.

A simple rule:

If the same problem appears repeatedly, stop treating it as an individual mistake.

Investigate the process.

Five Myths That Keep Revenue Cycle Problems Alive

Myth 1: Billing is responsible for all unpaid claims.

Reality: The cause may originate in registration, eligibility, authorization, documentation, coding, or payer processing.

Myth 2: Eligibility verification guarantees payment.

Reality: Active coverage does not establish that every service is covered or that all payment requirements have been met.

Myth 3: A clean claim guarantees reimbursement.

Reality: A technically correct claim can still be denied for coverage, medical necessity, authorization, or contractual reasons.

Myth 4: Hiring more billers solves recurring denials.

Reality: Additional staff may improve capacity, but they cannot permanently repair a defective workflow.

Myth 5: AI will eliminate medical billing problems.

Reality: AI can improve information processing, identify exceptions, and support staff. It cannot guarantee accurate source data, coverage, or reimbursement.

Buying more software to fix a broken process is like buying a faster printer because your documents are incorrect.

The printer is not the problem.

The Hidden Economics of Rework

Let's use a simple hypothetical example.

A practice submits 1,000 claims each month.

Suppose 10% require additional work because of preventable administrative or technical issues.

That means 100 claims need intervention.

If each intervention takes 20 minutes, the practice spends approximately 33 hours every month correcting those problems.

At an illustrative fully loaded labor cost of $30 per hour, that equals approximately $1,000 per month.

Over a year?

Approximately $12,000.

And that excludes delayed cash flow, additional follow-up, appeal preparation, unrecovered revenue, and management time.

The $40 rework figure sometimes cited in industry discussions should not be treated as a universal cost. Actual costs depend on the practice and the complexity of the claim.

But the economic principle remains.

A small administrative mistake becomes a recurring financial expense when the process never changes.

Root Cause Analysis: Stop Putting Buckets Under Leaking Roofs

Consider two different approaches.

Approach A: Reactive billing

A claim is denied.

The billing employee investigates.

The employee corrects the claim.

The claim is resubmitted.

The process repeats.

Approach B: Preventive workflow management

A claim is denied.

The practice investigates the root cause.

The team discovers that insurance verification is inconsistent.

The registration process is updated.

Staff receive training.

The practice monitors subsequent claims to determine whether the problem decreases.

Which approach creates lasting improvement?

Obviously, the second.

Yet healthcare often rewards the visible activity of working denials more than the less visible work of preventing them.

That needs to change.

A successful practice should not measure its billing department only by how many problems it resolves.

It should also measure how many recurring problems disappear.

AI in Medical Billing: Faster Errors or Better Decisions?

AI is becoming an increasingly important part of healthcare administration.

But let's challenge the usual marketing message.

Is the objective really to automate more billing tasks?

Or should we be asking why those tasks require so much manual intervention in the first place?

Traditional automation can:

Extract information.
Check required fields.
Flag inconsistencies.
Organize documents.
Generate reminders.
Route exceptions.
Produce reports.

These are useful capabilities.

But they do not automatically create workflow intelligence.

Imagine a patient with an insurance change, a service requiring authorization, and a subsequent claim denial.

The relevant information may exist in multiple systems.

A disconnected tool may identify a missing authorization number.

A better-connected workflow should help establish whether the insurance change was recognized, whether authorization was required, whether it was requested, whether approval matched the performed service, and whether supporting documentation was preserved.

That is a different level of operational understanding.

What AI Should Help With

Identify missing information.
Detect inconsistencies.
Organize authorization records.
Flag potential documentation gaps.
Recognize recurring denial patterns.
Summarize payer-specific issues.
Prioritize exceptions.
Support administrative decision-making.

What AI Should Not Do Without Appropriate Oversight

Invent clinical documentation.
Manufacture medical necessity.
Make unsupported coding decisions.
Guarantee coverage.
Guarantee payment.
Replace qualified compliance review.
Submit questionable claims without validation.

AI should remove administrative noise, not manufacture artificial confidence.

The ONNX Perspective: Don't Automate the Mess. Prevent the Mess.

This is the philosophy behind my work as a physician-entrepreneur and founder of ONNX.

Healthcare has invested heavily in downstream systems designed to process, review, correct, and resubmit claims.

But the deeper opportunity may be upstream.

The problem is often not that healthcare lacks information.

It is that information is fragmented.

Patient demographics exist in one system.

Insurance details exist in another.

Authorization information sits in a payer portal.

Clinical documentation resides in the EHR.

Billing staff are left to connect the dots.

And the patient often becomes the human bridge between disconnected systems.

That is not intelligent healthcare administration.

It is administrative improvisation.

ONNX is being developed as an upstream healthcare workflow intelligence platform, built by physicians and medical billing professionals.

Its objective is to help physician-owned practices identify and prevent avoidable administrative and billing problems before they become coding errors, claim rejections, denials, payment delays, and repetitive rework.

The underlying framework is:

Capture → Structure → Preserve → Propagate → Act → Learn

Capture: Collect accurate information early.

Structure: Organize it into consistent, usable data.

Preserve: Maintain the relevant context throughout the workflow.

Propagate: Make the right information available to the appropriate people and systems.

Act: Identify inconsistencies and address problems before they spread.

Learn: Use recurring patterns to improve the process.

This is not a promise to eliminate every denial.

Some denials are legitimate. Some require clinical judgment. Others reflect payer policy, contractual disputes, or coverage limitations.

The objective is more practical.

Reduce preventable administrative failures.

Improve information continuity.

Give physicians and their teams better visibility.

Reduce unnecessary manual work.

And allow people to spend more time on healthcare rather than chasing information.

A faster mop will not fix a leaking pipe.

Legal and Regulatory Considerations

Revenue cycle improvement must never come at the expense of compliance.

Practices should consider:

HIPAA: Protect patient information, maintain appropriate access controls, assess vendor security, and execute business associate agreements when required.

Accurate billing: Ensure claims reflect services actually provided and are supported by appropriate documentation.

False Claims Act: Avoid knowingly submitting false or unsupported claims.

Medicare and Medicaid: Follow applicable program-specific documentation, coding, coverage, and billing requirements.

Payer contracts: Understand filing deadlines, authorization requirements, appeal procedures, and reimbursement terms.

Appeals: Maintain documentation and track deadlines according to the applicable payer and program requirements.

Technology does not remove legal accountability.

AI-generated suggestions must be reviewed appropriately, and protected health information must be handled under applicable privacy and security requirements.

Ethical Considerations: Revenue Integrity Comes First

There is an important distinction between preventing inappropriate denials and manipulating claims.

The objective should be accurate reimbursement for appropriate care.

Not reimbursement at any cost.

Practices should never alter documentation to manufacture medical necessity, select unsupported codes, misrepresent services, or use automation to bypass appropriate clinical and compliance review.

Technology should protect the integrity of the medical record.

The best revenue cycle is not the one that gets every claim paid. It is the one that accurately represents the care delivered and supports legitimate reimbursement.

A Practical 30-60-90 Day Improvement Plan

Days 1–30: Discover

Review 90 days of claims.
Identify recurring denial categories.
Separate rejections from denials.
Examine eligibility and authorization workflows.
Interview front-desk and billing staff.
Establish baseline metrics.

Deliverable: Three high-priority workflow problems.

Days 31–60: Improve

Standardize registration.
Introduce pre-visit verification.
Centralize authorization tracking.
Improve claim validation.
Establish denial ownership.
Introduce weekly reviews.

Deliverable: Documented workflows and corrective actions.

Days 61–90: Measure

Compare denial rates.
Monitor first-pass resolution.
Track administrative rework.
Evaluate reimbursement timelines.
Review staff workload.
Assess technology effectiveness.

Deliverable: A performance report and updated improvement plan.

The goal is measurable progress, not an arbitrary promise of a 50% denial reduction.

The Future: From Revenue Cycle Management to Revenue Cycle Intelligence

The next generation of medical billing will not simply be about processing claims faster.

It will involve understanding the information behind those claims.

Expect growing emphasis on:

AI-assisted patient access.
Automated eligibility checks.
Better authorization coordination.
Payer-policy intelligence.
Interoperability.
Workflow analytics.
Human-supervised automation.
Predictive identification of administrative exceptions.

Smaller practices will need solutions that deliver meaningful intelligence without requiring enterprise-level budgets and administrative infrastructure.

The opportunity is not to replace every employee.

It is to eliminate unnecessary work that prevents employees from doing their jobs effectively.

The future belongs to systems that prevent avoidable problems, not just systems that report them faster.

Frequently Asked Questions

1. What is the difference between a claim rejection and a denial?

A rejection generally occurs before payer adjudication. A denial occurs after the payer processes the claim and determines that some or all of the requested payment will not be made.

2. What causes most medical billing rejections?

Common causes include incorrect patient information, invalid provider identifiers, missing data, incorrect payer routing, and technical submission errors.

3. What causes medical billing denials?

Common causes include coverage restrictions, eligibility issues, missing authorization, coding errors, medical necessity disputes, incomplete documentation, and timely filing violations.

4. Are medical billing denials preventable?

Some are. Administrative and technical errors may be prevented through better workflows. Legitimate coverage decisions and clinical circumstances cannot always be prevented.

5. Does insurance verification guarantee reimbursement?

No. Verification provides coverage information but does not guarantee payment for a particular service.

6. How can a small clinic reduce denials?

Improve registration, eligibility verification, authorization tracking, clinical documentation, coding validation, and root cause analysis.

7. Can AI eliminate medical billing denials?

No. AI can support error detection, information organization, and workflow improvement, but it cannot eliminate all payer disputes or guarantee reimbursement.

8. Should physicians hire more billers or invest in automation?

That depends on the underlying problem. First determine whether the practice has a capacity problem, a process problem, a data problem, or some combination.

9. What is the most important revenue-cycle metric?

There is no single metric. A useful dashboard includes clean claim rate, rejection rate, denial rate, first-pass resolution, days in A/R, and preventable denial rate.

10. Where should revenue-cycle improvement begin?

Start where information enters the practice. Accurate patient registration, eligibility verification, and authorization management can prevent problems from spreading downstream.

Final Thoughts: Stop Chasing Yesterday's Money

We have built an entire industry around correcting claims.

We have software to scrub claims, employees to investigate claims, departments to appeal claims, and dashboards to report claims.

Yet the same administrative mistakes continue to return.

Perhaps we should stop asking how to process unpaid claims faster.

Perhaps we should ask why the same problems keep happening.

A claim is the final product of a much larger chain of information.

When that information is incomplete, inconsistent, or disconnected, the consequences eventually appear in the billing department.

The problem is not always the claim. Sometimes the claim is simply where the problem becomes visible.

Physician-owned practices do not need another layer of administrative complexity.

They need better information, connected workflows, and technology that helps prevent avoidable mistakes.

A faster billing process is useful.

A process that creates fewer billing problems is better.

And a healthcare system that gives physicians more time to care for patients is worth building.

Get Involved: Let's Challenge the Way Healthcare Thinks About Billing

Here is my question for physicians, clinic owners, medical billers, and healthcare technology professionals:

If the mistake happens at the front desk, why does the billing department get the blame?

I want to hear your experience.

Where does your practice lose the most time and money: registration, eligibility, prior authorization, documentation, coding, or payer adjudication?

Comment below. Share one recurring billing problem you believe should have been prevented upstream.

Repost this article if you believe healthcare needs to spend less time correcting administrative mistakes and more time preventing them.

Let's move the conversation from denial recovery to denial prevention.

Let's challenge unnecessary complexity.

And let's give human attention back to healthcare.

About the Author

Dr. Daniel Cham is a physician, healthcare strategist, medical technology consultant, and entrepreneur. He is the founder of ONNX, an AI-powered medical billing and healthcare workflow intelligence initiative focused on helping physician-owned practices reduce avoidable administrative and billing problems.

His work explores the intersection of healthcare operations, medical billing, artificial intelligence, and revenue cycle management. He advocates for practical, human-supervised technology that improves information continuity, reduces administrative friction, and allows healthcare professionals to focus more attention on patient care.

Disclaimer and Professional Note

This article provides general educational information and does not constitute medical, legal, coding, compliance, or reimbursement advice. Applicable laws, payer policies, contracts, and coverage requirements may vary and change. Consult qualified professionals and authoritative sources before making practice-specific decisions.

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Question familiar practices. Challenge unnecessary complexity. Turn information into action.

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If this perspective resonates, consider reposting it to help more physicians and independent clinic owners rethink the way billing affects their practices.

References and Further Reading

1. Experian Health — 2026 State of Claims — Industry findings on claim denial trends, clean claims, payer policy changes, reimbursement challenges, and AI adoption.

2. Becker's Hospital Review — What's to Blame for Claim Denials in 2026? — Analysis of denial drivers, coverage requirements, coding inaccuracies, and revenue-cycle challenges.

3. Medical Economics — Why Are Claims Being Denied in 2026? — Discussion of uncovered services, payer restrictions, and reimbursement implications for medical practices.

4. The Denial Isn't the Problem. It's the Clue. — Why recurring denials can reveal deeper operational and information problems.

5. Your Medical Billing Problem May Not Be a Billing Problem: The Hidden Cost of Creating Work You Should Never Have to Do — How repetitive administrative work creates unnecessary costs for independent practices.

6. The Best Claim Is the One You Never Have to Fix — Why preventing billing errors upstream is more valuable than repeatedly correcting claims.

7. The Cheapest Claim Is the One You Never Have to Fix — The financial case for reducing avoidable claim rework.

#MedicalBilling #ClaimDenials #ClaimRejections #RevenueCycleManagement #HealthcareAI #ArtificialIntelligence #PhysicianEntrepreneur #IndependentMedicalPractice #PhysicianOwnedPractices #HealthcareInnovation #MedicalPracticeManagement #PriorAuthorization #HealthcareTechnology #RevenueIntegrity #PatientAccess #ONNX

Sunday, November 1, 2026

Your Clinic Doesn't Have a Denial Problem. It Has a System That Keeps Making the Same Mistakes.

Why the smartest revenue-cycle strategy isn't getting better at fixing claims. It's making fewer claims need fixing.

Congratulations. Your clinic has become very efficient at repeating its mistakes.

The same missing information. The same eligibility surprises. The same authorization confusion. The same documentation gaps. The same claim corrections.

Different patients. Different dates. Same problems.

Your billing team works harder. Your reports get longer. Your denial meetings become more sophisticated. And somehow, next month's problems look remarkably like last month's problems.

That isn't operational excellence.

It's an expensive treadmill.

Here's an uncomfortable question for every physician-owner and practice administrator:

What if your clinic doesn't have a denial-management problem at all? What if it has a problem that begins long before a claim is ever submitted?

We have spent years building better ways to repair revenue-cycle problems after they happen. We have invested in denial teams, follow-up queues, coding audits, claim scrubbers, and increasingly sophisticated dashboards.

But we rarely ask the more important question:

Why did the problem enter the system in the first place?

A claim that never needed correction is usually more valuable than a claim your team heroically rescues.

And that is where the conversation needs to change.

First, stop calling everything a denial.

One of the most expensive habits in medical billing is treating every unpaid, returned, or unresolved claim as if it belongs in the same bucket.

It doesn't.

A clearinghouse rejection and a payer denial are not interchangeable. They happen at different points, involve different decisions, and require different responses.

Yet many practices group them under one enormous umbrella called denial management.

That is a little like sending the fire department to investigate a smoke detector with a dead battery.

Both involve an alarm. They are not the same problem.

A clearinghouse rejection generally means a claim did not pass an electronic or administrative validation step before reaching the payer for adjudication. The issue might involve missing information, invalid formatting, an incorrect identifier, or another submission requirement.

A payer denial generally means the payer has received and adjudicated the claim, or otherwise issued a coverage or payment determination, and decided that payment is not appropriate as submitted.

The distinction matters because the corrective action is different.

A rejected claim may need to be corrected and resubmitted. A denied claim may require documentation, an authorization review, a corrected claim, reconsideration, or an appeal.

The exact response depends on the message, payer rules, and circumstances.

Common Claim Situations: What Happened and Where to Look

1. Claim rejected before adjudication

What may have happened: Submission or data validation failed.
Where to investigate: Practice management system, clearinghouse, and claim data.

2. Claim denied by payer

What may have happened: The payer determined that payment was not appropriate as submitted.
Where to investigate: Coverage, payer policy, authorization, coding, and clinical documentation.

3. Claim pending

What may have happened: The payer has not completed processing.
Where to investigate: Claim status, records requests, and payer processing workflow.

4. Claim returned for additional information

What may have happened: Required information is missing or incomplete.
Where to investigate: The payer's specific request and the supporting documentation.

5. Claim paid incorrectly or partially

What may have happened: The payment does not match the expected reimbursement.
Where to investigate: Contract terms, patient benefits, adjudication details, and adjustment information.

The takeaway: Different claim problems require different investigations. Before assigning a task to billing, identify what happened, where it happened, and where the underlying problem may have originated.

Electronic acknowledgments such as 999 and 277CA messages can help identify front-end submission issues. Electronic remittance advice, including the 835 transaction, can provide adjudication and adjustment information. But no single transaction tells the entire story. Payer portals, correspondence, explanation-of-benefits information, and other status sources may also be necessary.

Adjustment reason codes and remark codes must be interpreted in context. A code by itself is not a complete diagnosis of the operational problem.

The first rule of better revenue-cycle management is simple: classify the problem before assigning the work.

Otherwise, your team may be very busy solving the wrong problem.

Your billing team may not be the problem. Your system may be setting it up to fail.

Let's imagine a patient schedules an appointment.

The front desk enters the patient's insurance information. The eligibility check is completed. The patient arrives. The physician evaluates the patient and documents the encounter. The claim is coded, submitted, and eventually returned.

Then the billing team discovers that the coverage information was incomplete or that an authorization requirement was overlooked.

Who gets the task?

Billing.

Who gets the follow-up?

Billing.

Who gets measured on the unpaid claim?

Billing.

But where did the problem begin?

Possibly at registration. Possibly during scheduling. Possibly during eligibility verification. Possibly in a disconnected authorization process.

The person who discovers a problem is not necessarily the person—or department—that created it.

This is one of the most important distinctions in healthcare operations.

The location of the error is not always the location of its origin.

A practice can have excellent billers and still have a poorly designed revenue cycle.

It can employ experienced coders and still produce incomplete documentation.

It can own sophisticated software and still move fragmented information from one department to another.

Technology does not automatically create coordination. Sometimes it simply helps a disconnected system move information faster.

And when information is wrong, incomplete, or outdated, faster is not better.

It is just faster wrong.

The revenue cycle doesn't begin when the claim is submitted.

Many practices think of revenue-cycle management as something that happens after the encounter.

That is too late.

The financial outcome of an encounter is influenced by decisions made throughout the patient journey:

Registration → Scheduling → Eligibility → Authorization → Encounter → Documentation → Coding → Charge Capture → Claim Submission → Payer Processing → Payment → Reconciliation

Every transition is an opportunity to preserve information—or lose it.

Consider a few examples.

At registration: An incorrect member ID may travel through multiple systems before anyone notices.

During eligibility: Coverage may be active, but that does not automatically establish that a particular service is covered or that all requirements have been satisfied.

During authorization: A service may require prior authorization, but the approval may not match the actual service, date, provider, or location.

During documentation: The physician may have captured the clinical reasoning, but the documentation may not clearly support the level of service or the specific work performed.

During coding: The coder may be forced to interpret incomplete or ambiguous information.

Before submission: A claim may pass a basic scrubber and still contain an underlying coverage, documentation, or policy problem.

The problem is not always that someone failed to do their job.

Sometimes the problem is that the system never gave that person the complete information needed to do it correctly.

This is the difference between a workflow that merely moves data and one that preserves context.

A patient record should not become a collection of disconnected facts that each department must reconstruct.

The goal should be to make the right information available at the right decision point—not to ask the next person to rediscover it.

The front desk is part of the revenue cycle. Whether anyone admits it or not.

Let's challenge another comfortable assumption.

The front desk is not merely an administrative function.

It is one of the earliest financial control points in a medical practice.

The information captured during registration can influence eligibility, authorization, claim accuracy, patient responsibility, and downstream administrative work.

That does not mean front-desk employees should be expected to become coding experts or insurance-policy specialists.

It means practices should stop treating registration as a low-consequence clerical activity.

A few questions can make a meaningful difference:

Was the correct insurance plan identified?
Was the member information verified?
Was the patient's coverage checked for the relevant date?
Were coordination-of-benefits issues identified?
Does the planned service require authorization or referral?
Is the authorization associated with the correct provider, location, and service?
Is the information available to the people who need it later?

The answer cannot simply be, "We checked insurance."

Checked what? When? Against which information? Was the result retained? Did anyone act on an exception?

A checkbox that says verified is not the same as a reliable verification process.

Documentation of a task is not proof that the task produced the intended result.

The more disconnected the workflow, the more likely the next employee will repeat the work—or discover that it was never completed properly.

And repeated work is not free.

It consumes staff time, delays claims, creates patient confusion, and makes performance harder to measure.

Prior authorization: the administrative obstacle course nobody signed up for.

Prior authorization deserves special attention because it illustrates how upstream problems can create downstream consequences.

A practice may obtain an authorization and still encounter a problem later.

Why?

The authorization may not match the final service. The service date may change. The rendering provider may differ. The location may be incorrect. The approval may have expired. The payer's requirements may have changed. The authorization number may not have been carried into the claim workflow.

In other words, obtaining approval is not necessarily the end of the process.

It is one event in a chain of information that must remain accurate.

The American Medical Association has reported substantial administrative burdens associated with prior authorization in its physician surveys. Its 2024 survey, for example, reported an average of 39 prior authorization requests per physician per week and approximately 13 hours of physician and staff time spent on prior authorization activities. The AMA has also reported that physicians widely believe prior authorization can delay necessary care.

These findings should make every practice owner ask a difficult question:

How much of your staff's time is spent advancing patient care—and how much is spent proving that someone already approved it?

Prior authorization is not merely a billing issue. It can affect scheduling, patient access, clinical decision-making, and the ability to deliver care on time.

The operational answer is not to tell employees to work harder.

It is to connect the authorization process to the actual encounter and claim, preserve the approval details, identify mismatches early, and make exceptions visible before submission.

The best authorization workflow is not the one that produces the most approval numbers.

It is the one that prevents an approval from becoming useless because the information was lost, changed, or disconnected.

The hidden cost of a claim is often the work nobody counts.

Most practices can tell you how much they collected last month.

Fewer can tell you how much time they spent correcting information that should have been accurate before the claim was submitted.

That is a blind spot.

Consider a hypothetical clinic submitting 1,000 claims each month.

If 8% require avoidable correction, that is 80 claims.

If each correction takes 15 minutes, the practice spends 20 hours each month on that work.

Now imagine reducing the avoidable correction rate from 8% to 4%.

That is 40 fewer corrections, or 10 hours of staff time recovered each month.

This is an illustration, not an industry benchmark. Actual costs depend on staff compensation, complexity, payer requirements, and the time required for each correction.

But the principle is important.

The financial impact of a claim problem is not limited to the amount that was denied.

It may include:

Staff time spent identifying the issue.
Time spent researching the original encounter.
Repeated phone calls or portal checks.
Corrected claims and resubmissions.
Delayed reimbursement.
Patient calls and billing confusion.
Management time spent reviewing exceptions.
Additional work caused by incomplete handoffs.

And there is another cost: opportunity.

Every hour spent reconstructing yesterday's information is an hour that cannot be spent improving tomorrow's workflow.

A recovered claim is a financial success. A preventable claim problem is an operational failure that never needed to happen.

Both deserve attention. But they should not receive equal applause.

The five-whys test: stop treating symptoms as root causes.

When a claim problem occurs, ask why.

Then ask why again.

And again.

Imagine a claim is denied because an authorization was missing.

Why was the authorization missing?

Because the claim was submitted without an authorization number.

Why was the number missing?

Because it was not available in the billing system.

Why was it not available?

Because the authorization was recorded in a separate workflow.

Why was the workflow separate?

Because scheduling and authorization do not share a reliable handoff.

Why was there no reliable handoff?

Because the practice designed the process around departmental tasks rather than the complete patient-to-claim journey.

Now we have a different problem.

The immediate correction is to obtain or locate the authorization information. The operational correction is to redesign the handoff.

If the practice only fixes the claim, the same issue may return next week.

If it fixes the process, it may prevent many future claims from encountering the same problem.

This is why denial codes should be treated as signals—not as the final explanation.

A code tells you something about how a claim was processed. It does not automatically tell you why the information was missing, incorrect, or inconsistent.

A denial report is not a root-cause analysis. It is a place to begin one.

Three expert lenses that should change how practice owners think.

There are three useful perspectives to bring together when evaluating revenue-cycle performance. These are professional lenses, not quotations attributed to individual experts.

The physician's lens: clinical integrity.

The physician needs documentation that accurately reflects the care delivered and the reasoning behind clinical decisions. The answer to a billing problem must never be to distort the medical record or document services that were not performed.

Clinical documentation should support appropriate coding while remaining a truthful account of the encounter.

The medical biller's lens: operational accuracy.

The biller sees what happens when information is incomplete, inconsistent, or disconnected. The biller often recognizes recurring patterns across payers, claim types, and departments.

That knowledge should not be used only to clear work queues. It should inform process improvement upstream.

The practice administrator's lens: system performance.

The administrator needs to understand where work accumulates, where handoffs fail, how long exceptions remain unresolved, and which recurring problems consume the most resources.

A dashboard showing the number of claims worked is useful. A dashboard showing why claims repeatedly require intervention is more useful.

The strongest revenue-cycle strategy combines all three perspectives.

Clinical accuracy. Billing intelligence. Operational accountability.

When those perspectives operate in isolation, the practice gets more handoffs.

When they work together, the practice gets better decisions.

AI will not rescue a broken workflow. It may simply automate the confusion.

Artificial intelligence is entering nearly every part of healthcare administration.

It can help identify patterns, extract information, compare documentation, detect inconsistencies, prioritize exceptions, and support repetitive administrative tasks.

That is promising.

But there is a dangerous assumption emerging in the market:

If a practice adds AI, its revenue cycle will automatically become more efficient.

Not necessarily.

AI can identify an inconsistency. But if the underlying information is incomplete, it may not know which source is authoritative.

AI can flag a missing authorization. But if authorization information is stored in an inaccessible system, the problem remains.

AI can suggest a code. But it should not invent clinical facts or replace the physician's responsibility for accurate documentation.

AI can prioritize a denial queue. But prioritizing a recurring problem is not the same as preventing it.

Automation is not the same as intelligence.

A system that automatically repeats a bad process is not intelligent. It is merely consistent.

The more useful question is not, "How many tasks can AI automate?"

It is:

Can the system recognize a problem before it becomes a claim problem—and help the right person make the right decision?

That requires more than a chatbot or a claim scrubber.

It requires structured information, preserved context, reliable rules, appropriate human oversight, and feedback from actual outcomes.

AI should support decisions, not manufacture certainty.

And in healthcare, the ability to explain why a recommendation was made matters just as much as the recommendation itself.

The new competitive advantage: fewer surprises.

A predictable revenue cycle is not one in which every claim is paid immediately.

Payers have coverage rules. Contracts vary. Medical necessity requirements differ. Documentation must support the service. Some claims will legitimately require review, correction, or appeal.

No software can eliminate every denial.

But a practice can reduce avoidable surprises.

It can identify recurring eligibility issues. It can detect missing authorization information. It can improve documentation handoffs. It can distinguish front-end rejections from payer adjudication problems. It can identify which exceptions require human judgment.

The objective is not perfection.

It is fewer preventable problems, clearer accountability, and less repeated work.

This requires measuring more than collections.

Consider tracking:

Front-end rejection rate: How many claims fail before payer adjudication?
First-pass acceptance rate: How many claims pass initial submission requirements?
Denial rate: How many adjudicated claims receive a denial?
Avoidable denial rate: How many denials could reasonably have been prevented?
Correction rate: How often must information be repaired or resubmitted?
Authorization mismatch rate: How often does the authorization fail to align with the actual service?
Days in accounts receivable: How long does reimbursement remain outstanding?
Rework hours: How much staff time is spent correcting recurring problems?
Root-cause recurrence: Are the same issues returning after corrective action?

Definitions matter. Practices should establish consistent denominators, time periods, and claim populations before comparing performance.

A lower denial rate is useful.

A lower denial rate combined with less rework and faster resolution is more meaningful.

And a lower denial rate achieved without compromising clinical documentation or appropriate billing is the real objective.

A practical 90-day plan: fix the process before buying another dashboard.

A small or medium physician practice does not need a massive transformation project to begin improving its revenue cycle.

It needs a clear starting point.

Days 1–30: Find the recurring problems.

Start by reviewing recent rejected, denied, delayed, and corrected claims.

Do not combine them into one category.

Separate front-end submission issues from payer adjudication outcomes. Identify the most frequent reasons. Look for patterns by payer, service type, provider, location, and point in the workflow.

Ask staff where they repeatedly lose time.

Ask billers which problems they recognize before they even open the claim.

Ask the front desk which insurance or authorization issues are difficult to resolve.

The goal is to identify the five most common avoidable problems—not to produce a 40-page report nobody reads.

Days 31–60: Repair the handoffs.

For each recurring problem, identify:

Where the information should have been captured.
Who was responsible for capturing it.
Where it should have been stored.
Who needed access to it.
When the issue should have been identified.
What should happen when the information is missing or inconsistent.

Create a clear exception process.

Do not rely on an employee remembering a verbal instruction from three weeks ago.

Make the process visible, repeatable, and measurable.

Days 61–90: Measure whether the changes worked.

Compare the results with the original baseline.

Did front-end rejections decrease? Did avoidable denials decrease? Did staff spend less time correcting claims? Did authorization mismatches decline? Did the same problems stop recurring?

If the numbers did not improve, investigate why.

A new policy is not proof of a better process.

A training session is not proof of behavior change.

A dashboard is not proof of operational improvement.

The only meaningful question is whether the practice is producing better outcomes with less preventable work.

Clearinghouse redundancy: hope is not a business-continuity strategy.

The 2024 Change Healthcare cyberattack exposed how dependent much of the healthcare system had become on critical revenue-cycle infrastructure.

For many practices, the disruption was not an abstract technology story. It affected claims, payments, eligibility, and daily operations.

The lesson was not simply that a particular vendor can fail.

The lesson was that a practice needs to understand its dependencies.

What happens if its clearinghouse becomes unavailable?

Can staff identify which claims were transmitted and which were not? Can the practice access its own claim data? Are there documented procedures for status checks and resubmissions? Does the practice know how to avoid duplicate submissions when service is restored?

A backup clearinghouse arrangement may be appropriate for some organizations, but redundancy is not as simple as signing up for another vendor.

Connectivity, payer enrollment, configuration, transaction support, workflows, and data access must be considered.

A second vendor that has never been configured or tested is not a backup plan.

It is a comforting line on a spreadsheet.

Practice owners should review business-continuity arrangements with their vendors and qualified technical or compliance advisers.

The broader principle applies beyond clearinghouses:

If one disconnected system can stop your revenue cycle, you do not fully control your revenue cycle.

Regulation is changing. Your information architecture should be ready.

Healthcare administrative requirements continue to evolve.

CMS's Interoperability and Prior Authorization Final Rule, CMS-0057-F, establishes requirements affecting certain payers, including provisions related to prior authorization processes and interoperability. Several major implementation milestones begin in 2027, with requirements and timelines varying by provision and payer type.

The rule is part of a broader effort to improve electronic access to information and reduce administrative friction.

Practices should not assume that every requirement applies directly to every physician office in the same way. Applicability depends on the entity, payer, transaction, and specific provision.

But there is a practical lesson for every practice owner:

Information that cannot be found, exchanged, or interpreted reliably will become an increasingly expensive operational weakness.

Practices should understand what information their systems capture, how it is structured, how it is exchanged, and whether the information remains usable throughout the patient and claim lifecycle.

Regulatory readiness should not be treated as a last-minute software purchase.

It should be part of the way a practice manages information.

This is not legal advice. Practices should consult current CMS guidance, payer requirements, and qualified compliance counsel when assessing their obligations.

Five myths that keep revenue-cycle problems alive.

Myth 1: If the claim was submitted, our job is done.

Reality: Submission is a milestone, not a financial outcome. Claims still require acknowledgment, status monitoring, adjudication review, and appropriate follow-up.

Myth 2: Denials are primarily a billing department problem.

Reality: Some problems originate in billing. Others begin in registration, scheduling, eligibility, authorization, clinical documentation, or system configuration.

Myth 3: A clean claim scrubber means the claim is correct.

Reality: A claim can pass technical validation and still contain a coverage, authorization, documentation, or payer-policy problem.

Myth 4: More automation means fewer errors.

Reality: Automation can reduce repetitive work, but it can also reproduce incorrect information at scale. Data quality, rules, monitoring, and human oversight matter.

Myth 5: We need more people working the denial queue.

Reality: Additional staff may be necessary, but hiring more people to repeatedly correct the same preventable problem can make an inefficient process more expensive.

The point is not to eliminate denial management.

The point is to stop confusing the repair department with the prevention strategy.

The legal and ethical boundary: never let revenue pressure rewrite clinical reality.

There is an important distinction between improving documentation and manipulating documentation.

A physician should accurately document the services provided, the medical decision-making performed, and the clinical reasoning supporting the encounter.

A practice should not encourage upcoding, unsupported diagnoses, retrospective embellishment, or documentation designed to manufacture medical necessity.

Coding assistance and AI-generated suggestions must be reviewed under appropriate policies and workflows. The medical record must remain accurate, attributable, and consistent with applicable requirements.

Patient information must also be protected. Systems handling protected health information need appropriate privacy, security, access controls, vendor arrangements, and compliance oversight.

Revenue-cycle optimization should never come at the expense of patient trust or clinical integrity.

The purpose of better billing is to represent the care that was actually delivered—not to change the story to obtain payment.

That principle is not negotiable.

What should the next generation of revenue-cycle technology actually do?

The next generation of healthcare administration should not simply produce more alerts, more dashboards, and more tasks.

It should help practices understand what is happening across the entire workflow.

Imagine a system that recognizes an eligibility discrepancy before the patient arrives.

It identifies an authorization requirement before the service is performed.

It preserves the authorization details so the information is available when the claim is created.

It identifies a documentation gap while the encounter is still fresh.

It gives the appropriate person a clear exception to review.

It learns from the outcome and identifies whether the same issue is recurring.

That is a different approach from waiting for a denial and then assigning someone to investigate it.

The distinction is between reacting to individual claims and understanding the conditions that produce claim problems.

The future should move from isolated tasks to connected decisions.

From fragmented information to preserved context.

From repeated correction to prevention.

From reactive billing to more deterministic revenue operations.

Not every decision should be automated. Not every exception can be predicted. But the system should make preventable problems easier to see and harder to repeat.

The OnnX perspective: move intelligence upstream.

This is the problem space behind OnnX 2.0.

OnnX is an upstream intelligence platform for healthcare, built by physicians and medical billers, to reduce downstream administrative and billing noise so physicians can focus on clinical care.

The premise is straightforward:

Many billing problems do not begin when a claim is submitted.

They begin when information is missing, fragmented, misunderstood, or disconnected earlier in the patient journey.

By the time the problem appears as a coding question, authorization issue, claim rejection, denial, or payment delay, the practice may already have spent time and resources creating and correcting it.

OnnX is built around a different operating principle:

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

Capture the relevant information.

Structure it so it can be understood.

Preserve the context so it does not disappear between departments.

Propagate it to the people and systems that need it.

Act when an exception or decision requires attention.

Learn from outcomes to improve future decisions.

The ambition is not to become another traditional revenue-cycle outsourcing service.

It is to help practices make better decisions before preventable problems travel downstream.

Built by physicians and medical billers since 1995. OnnX 2.0 is focused on what happens before the claim becomes a problem.

Because the most efficient claim is not the one that gets rescued.

It is the one that never needed rescuing.

The question every physician-owner should ask.

Your billing team may be collecting money, correcting claims, appealing denials, and keeping the practice financially alive.

Give them credit.

But do not mistake their ability to recover from a broken process for evidence that the process works.

A practice should not measure success only by how many problems its employees can resolve.

It should also measure how many problems the organization no longer creates.

The next breakthrough in medical billing may not come from working the denial queue faster.

It may come from asking why the same claims keep ending up there.

If your clinic could eliminate one recurring revenue-cycle problem before it ever reached the billing team, which problem would you choose—and why is it still happening?

Share your experience in the comments. I am especially interested in hearing from physician-owners, practice administrators, and medical billers who see these problems firsthand.

If this perspective challenges the way your organization thinks about denial management, share or repost it. The conversation needs to move upstream.

About the Author

Dr. Daniel Cham is a physician and medical consultant with expertise in medical technology consulting, healthcare management, and medical billing. He focuses on delivering practical insights that help professionals navigate complex challenges at the intersection of healthcare and medical practice.

Disclaimer

This article is for educational and informational purposes only. It does not constitute legal, coding, billing, reimbursement, compliance, or medical advice. Rules and payer requirements vary and change over time. Practices should verify applicable requirements with authoritative sources, payers, and qualified professional advisers.

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References

American Medical Association — Prior Authorization
Physician survey findings, administrative burden, and resources on prior authorization reform.
Centers for Medicare & Medicaid Services — Interoperability and Prior Authorization Final Rule (CMS-0057-F)
Official information on federal interoperability and prior authorization requirements, implementation milestones, and affected payer organizations.
U.S. Department of Health and Human Services — Change Healthcare Cybersecurity Incident
Federal updates and resources concerning the cybersecurity incident and its effects on healthcare operations.

The Denial Isn’t the Problem. It’s the Clue.
Why a denial may reveal a deeper operational problem that began before claim submission.
Your Medical Billing Problem May Not Be a Billing Problem: The Hidden Cost of Creating Work You Should Never Have to Do
The hidden operational cost of fragmented information, repeated corrections, and preventable administrative work.
The Best Claim Is the One You Never Have to Fix
A closer look at upstream intelligence and the shift from reactive claim correction to prevention.

AI-Driven Medical Billing Explained | Faster Reimbursements, Fewer Denials
Explore how AI and automation may change medical billing—and why preventing problems matters more than simply processing them faster.

The Health Momentum
Explore additional perspectives on healthcare, medical practice, and revenue-cycle challenges.

#MedicalBilling #RevenueCycleManagement #HealthcareTechnology #PhysicianPractice #MedicalPracticeManagement #HealthcareAI #PriorAuthorization #ClaimsManagement #DenialManagement #OnnX2