Wednesday, October 28, 2026

Your Medical Billing Problem May Not Be a Billing Problem: The Hidden Cost of Creating Work You Should Never Have to Do

What if the biggest opportunity in medical billing isn’t getting denials overturned—but making fewer denials necessary?



“Accurate data and the right technology can help identify problems upstream, reduce manual intervention and give revenue cycle teams a better opportunity to prevent avoidable denials before claims are submitted.” — Mindy Fortson, Chief Operating Officer, Experian Health

 

The Most Expensive Work in Your Clinic Might Be Work That Should Never Have Existed

It is Monday morning.

Your front-desk coordinator is answering three phone calls. A patient has arrived early. Another patient has forgotten an insurance card. The physician is already running behind.

Someone checks the patient in. Someone else updates the chart. An appointment gets moved. A detail gets missed.

Nobody is careless. Nobody is sitting around doing nothing.

Everyone is busy doing the work.

Three weeks later, a claim comes back denied because a required authorization was not obtained.

Now the biller has a new assignment.

Investigate the denial. Find the original encounter. Contact the payer. Determine whether the problem can be corrected. Request documentation. Submit an appeal if appropriate. Track the outcome.

The physician may be asked for additional information. The front desk may need to reconstruct what happened. The practice manager may get involved.

One missed step has become five new tasks.

And here is the question nobody may have time to ask:

Why are we spending so much time fixing a problem that might have been prevented before the appointment?

This is where medical billing conversations often go wrong.

We discuss faster claim submission, smarter denial management, more powerful dashboards, and increasingly sophisticated AI.

Those things can be useful.

But what if we are getting exceptionally good at correcting problems that our own workflows keep creating?

A faster billing department can process more work. A better denial-management system can organize more denials. An AI agent can chase more missing information.

But if the same preventable mistakes keep entering the system, we may simply be building a faster machine for producing administrative rework.

Automating unnecessary work is still unnecessary work.

That is the uncomfortable question for physicians and independent clinic owners: Are we improving the revenue cycle, or are we becoming more efficient at managing its avoidable failures?


The Contrarian View: Your Billing Team May Be Treating Someone Else's Symptoms

Medical billing is often blamed when reimbursement is delayed.

Sometimes, the billing team really is the source of the problem. A claim may be coded incorrectly, submitted with incomplete information, or left in a work queue too long.

But many problems originate elsewhere.

A demographic detail may be wrong at registration. Insurance coverage may not have been verified appropriately. A required authorization may not have been secured. Documentation may not support the submitted claim. A handoff may fail because nobody owns the next step.

The biller often discovers the problem only after it has traveled through several systems.

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

Think about that the next time someone suggests solving every billing problem by hiring another biller.

More capacity may be necessary. But before increasing capacity, investigate whether the practice is asking its existing staff to repeatedly compensate for the same broken process.

A useful distinction:

  • Correction repairs a specific error.
  • Prevention changes the conditions that allow the error to recur.
  • Automation performs or supports a task.
  • Intelligence helps determine whether that task is necessary, what information it requires, and when intervention is appropriate.

These are different things.

A clinic needs all four at different times. The mistake is assuming that automation alone will solve a problem rooted in process design or data quality.


A Small Clinic Does Not Need a Hospital-Sized Solution

Imagine a three-physician practice with one biller, two front-desk employees, and a practice manager who also handles staffing, scheduling, and compliance.

The practice experiences recurring claim rejections.

A consultant recommends an advanced analytics platform, a dedicated denial-management department, and a custom AI deployment.

The recommendations may sound impressive. They may even make sense for a large health system.

But the clinic owner has a more immediate question:

Who is going to implement all of this while we are trying to see patients and get paid?

This is where operational advice loses credibility. It identifies a real problem but prescribes a solution that ignores the resources of the people expected to use it.

A solution that is theoretically excellent but operationally impossible is not a practical solution.

For a smaller practice, the first steps may be much simpler:

  • Review the claim-validation features already available in the EHR or practice management system.
  • Identify the five most common recurring billing problems.
  • Hold a 30-minute weekly review with the people closest to the workflow.
  • Use the Five Whys technique to investigate one recurring problem.
  • Track a few meaningful metrics instead of maintaining a dashboard that resembles an aircraft cockpit.
  • Assign one person responsibility for each corrective action.

No new department required.

No elaborate AI architecture required.

No 47-tab spreadsheet that only one employee understands.

The objective is not to make small clinics imitate large hospitals.

It is to give small clinics the right level of operational intelligence for their own needs.

Small teams do not need fewer insights. They need fewer distractions between insight and action.


The Numbers: Denials Are a Warning Signal, Not a Complete Diagnosis

Experian Health's 2026 State of Claims survey, published October 28, 2026, provides a timely snapshot of industry concerns.

The survey of 204 healthcare professionals responsible for financial, billing, or claims management decisions found that:

  • 75% reported that claim denials were increasing.
  • 40% said denial rates exceeded 10% of claims.
  • 65% reported that submitting clean claims had become more difficult.
  • 64% said reimbursement timelines were getting longer.
  • 89% identified reducing denials as a priority.

These are survey results, not a census of all U.S. practices. They should not be interpreted as the expected performance of every specialty or independent clinic.

Still, they reveal a troubling pattern: providers are investing attention in denial reduction while reporting growing complexity in the processes that produce claims.

 

The wrong response is to see these numbers and immediately conclude that every practice needs a new AI product.

The better response is to ask what is driving the problem in your own organization.

Are claims failing because of missing information? Are payer requirements changing? Are authorization workflows unreliable? Are coding issues recurring? Are staff spending too much time navigating portals and checking status?

The numbers identify pressure. Your own operational data must identify the cause.

An industry benchmark tells you where to look. It does not tell you what is broken in your clinic.


Prior authorization: A deadline is not the same as a completed process

In a September 2026 poll, the Medical Group Management Association reported that 44% of responding medical group leaders said prior authorization turnaround had become slower in 2026 compared with 2025. Only 7% reported faster turnaround; 40% said it was about the same, and 9% were unsure.

The poll had 178 applicable responses. The findings describe respondents' reported experiences, not every payer or practice.

 

The deeper lesson is that the payer's decision time is only one part of the administrative journey.

Staff may still need to determine whether authorization is required, identify the correct submission channel, assemble supporting documentation, check status, respond to requests, and manage appeals.

A shorter clock does not automatically eliminate those tasks.

Measuring the time a payer takes to decide is not the same as measuring the total time a clinic spends getting an answer.


Administrative automation: Opportunity does not equal return on investment

The 2024 CAQH Index estimated a $20 billion opportunity to simplify healthcare administration through greater use of automated transactions. It also estimated that automation helped the industry avoid $222 billion in administrative costs in 2023, using its own methodology.

These figures cover healthcare administration broadly. They are not a promise that a particular billing product will save a particular clinic a certain amount.

 

The practical question is not whether automation has value. It clearly can.

The question is whether a specific intervention solves a documented problem at a cost the practice can justify.

A technology investment should be measured against the work it eliminates, the errors it prevents, the time it returns to staff, and the risks it introduces.

Otherwise, a projected return on investment is just optimism wearing a spreadsheet.


Recent News: Healthcare Is Automating, but the Upstream Problem Remains

Three current developments are relevant to the future of medical billing.

1. Providers are looking to AI to address denials

Experian Health's October 28 survey found that 84% of respondents believed AI could improve claims management in some capacity. Nearly two-thirds said they definitely or probably planned to invest in AI for claims and denial management.

That interest is understandable. Staff are dealing with complex payer rules, recurring corrections, and increasingly demanding workflows.

But AI should not be treated as a universal remedy.

If inaccurate information enters a system, AI may detect it, explain it, or move it downstream more efficiently. Prevention requires a process that captures the right information at the right time.

The opportunity is to combine useful automation with better source data, appropriate validation, and clear ownership.

2. Prior authorization requirements are evolving

The Centers for Medicare & Medicaid Services' 2024 Interoperability and Prior Authorization Final Rule includes provisions intended to improve electronic information exchange and make certain prior authorization processes more transparent.

Many operational provisions generally began in 2026, while API implementation requirements for impacted payers generally begin in 2027. Exact requirements and compliance dates vary by payer category and provision.

The rule creates opportunities for better electronic workflows, but clinics should not assume that every payer, plan, or service follows the same requirements.

A practice should verify its current obligations, ask vendors about relevant functionality, and maintain a clear process for exceptions.

3. More electronic exchange will not automatically mean better data

Interoperability is important. Systems need to exchange relevant information without forcing staff to repeatedly re-enter the same details.

But moving information electronically does not guarantee that it is accurate.

A wrong insurance identifier can move between systems just as efficiently as a correct one. An outdated workflow can remain outdated even when it is automated.

A connected system can distribute an error faster than an unconnected system.

That is not an argument against interoperability. It is an argument for pairing interoperability with data validation, process ownership, and feedback loops.

The future should not be defined by how many systems can communicate. It should be defined by whether those systems help the right information reach the right person before the mistake becomes expensive.


Three Expert Perspectives: Different Angles, One Practical Lesson

Rather than inventing quotations or implying that experts endorse a particular product, the following perspectives interpret published survey findings and regulatory guidance.

Perspective 1: Mindy Fortson, Chief Operating Officer at Experian Health

Fortson's comments accompanying the 2026 State of Claims survey emphasized accurate data and appropriate technology as ways to identify problems upstream and reduce manual intervention.

What clinic owners can take from this: Before purchasing another denial-management tool, investigate which recurring errors could be identified before claim submission.

Review existing validation features. Check whether staff can see and resolve the warnings. Establish who owns exceptions.

An alert without an assigned action is merely a more sophisticated way of displaying a problem.

Perspective 2: The CMS interoperability framework

CMS's interoperability and prior authorization rule aims to improve data exchange and streamline specified administrative processes.

What clinic owners can take from this: Treat authorization as a process with a beginning, a decision, and a verifiable outcome—not simply a request that someone submitted.

Record the relevant request, status, decision, and conditions. Establish what evidence is required before a service proceeds under the practice's applicable policies and payer requirements.

Requested is not approved. Approved is not necessarily the end of every coverage and claim requirement.

Perspective 3: MGMA's practice-management findings

MGMA's 2026 poll suggests that the authorization burden can persist even when formal decision timelines are defined.

What clinic owners can take from this: Measure the entire workflow, including preparation, submission, status checks, additional documentation, and follow-up.

If staff spend more time checking status than preparing the request, the problem may not be the initial submission process.

If requests are repeatedly delayed because documentation is incomplete, a better preparation checklist may be more useful than another status-tracking dashboard.

The three perspectives lead to a shared conclusion:

The best intervention is the one that addresses the actual bottleneck—not the one with the most impressive feature list.


Myth Busters: Five Popular Ideas Worth Questioning

Myth 1: More software means fewer denials

Not automatically.

A new product may offer valuable capabilities, but its impact depends on workflow fit, data quality, configuration, integration, training, and ongoing oversight.

Reframe: Do not ask, “What software should we buy?” Ask, “Which recurring failure should this software prevent?”

Myth 2: Billing owns the entire revenue cycle

Billing may discover the problem, but it may not control registration, eligibility, authorization, documentation, or the handoff that caused it.

Reframe: The revenue cycle is a chain of responsibilities. The last person to touch the claim cannot be accountable for every weakness in the chain.

Myth 3: AI will eliminate administrative errors

AI can help identify patterns, flag missing fields, and automate repetitive tasks. It can also miss issues or produce incorrect recommendations.

Reframe: AI should reduce preventable work while preserving appropriate verification—not turn unverified assumptions into automated decisions.

Myth 4: A submitted claim is a completed task

Submission is a milestone, not proof of successful reimbursement.

The claim may be rejected, denied, underpaid, or left unresolved.

Reframe: Define completion by the verified outcome, not by the click that initiated the process.

Myth 5: More metrics mean better management

A dashboard with 50 indicators can make a practice feel informed while leaving staff unsure what to do next.

Reframe: The purpose of a metric is not to fill a dashboard. It is to improve a decision.


Follow the Claim, Not Just the Denial

One of the most useful changes a clinic can make is to organize its review around the chronological claim lifecycle.

Instead of treating every error code as an isolated event, trace the process from the original encounter to the final financial outcome.

Step 1: Registration

The patient's information is collected.

Ask whether the intake process captures the information the practice needs, whether exceptions are handled consistently, and whether someone owns missing details.

A small omission can become a downstream problem.

Practical action: Review the registration checklist and identify the information gaps that repeatedly cause corrections.

Step 2: Eligibility and benefits

Coverage information is checked, but eligibility is not a guarantee of payment for a particular service.

Practical action: Define when verification occurs, what staff should confirm, and how exceptions are escalated. Make sure the team understands the limitations of the response it receives.

Step 3: Authorization and referrals

Requirements vary by payer, plan, service, and circumstances.

Practical action: Maintain current payer-specific procedures. Record requests and decisions, and ensure the responsible person can distinguish a submitted request from an approval.

Step 4: Documentation and charge capture

Clinical documentation must accurately reflect the care provided and support the billing process.

Practical action: Review recurring documentation gaps with the relevant staff. Determine whether the problem involves unclear expectations, a poorly designed template, training, or a breakdown in the handoff.

The answer is not to create more documentation for its own sake. It is to ensure the right information is available for the right purpose.

Step 5: Coding and claim validation

Coding should reflect the documented service and applicable requirements. Automated edits can flag certain inconsistencies but cannot guarantee coverage or payment.

Practical action: Review the claim-validation functions already available in the practice's systems. Check whether edits are appropriate for the specialty and payer mix.

Step 6: Clearinghouse acceptance and rejection

A claim may fail technical or administrative checks before it reaches normal payer adjudication.

Practical action: Ensure staff understand the distinction between a rejection and a denial. Track unresolved rejections, assign an owner, and establish a timely correction process.

Step 7: Payer adjudication

The payer evaluates the claim under applicable rules and circumstances.

Practical action: Record the reason for a denial, its financial significance, the next action, the responsible person, and the relevant deadline. Determine whether correction, resubmission, reconsideration, or appeal is appropriate.

Not every denial is preventable or recoverable.

Step 8: Payment and reconciliation

The practice reviews what was paid, what remains outstanding, and whether the result matches expectations.

Practical action: Reconcile remittance information, adjustments, patient responsibility, and unresolved balances according to established procedures.

The goal is not merely to get claims out the door.

The goal is to make the journey from encounter to financial resolution more reliable.


A Practical 30-Minute Weekly Review

A small practice does not need a dedicated analytics department to learn from recurring billing problems.

It can start with a short meeting involving the biller, front desk, and practice manager, with other staff involved when appropriate.

Minutes 1–5: Choose the problem

Review recent rejections, denials, delays, or repeat corrections.

Choose one or two issues with meaningful recurrence, financial impact, urgency, or patient consequences.

Minutes 6–15: Find where the problem began

Use the Five Whys technique.

Suppose a claim was denied because authorization was missing.

Why? It was not obtained before the service.

Why? The scheduling process did not flag the requirement.

Why? Staff relied on memory rather than a shared checklist.

Why? No one maintained the checklist when requirements changed.

Why? The practice had not assigned ownership of that workflow.

This is a hypothetical example. The team must verify the actual cause rather than assume the explanation.

The important discovery is that the problem may involve a checklist and an assigned owner—not simply another round of claim correction.

Minutes 16–25: Make one change

Update the checklist, clarify a handoff, configure an appropriate validation feature, or improve task tracking.

Assign one person responsibility for the action and set a completion date.

Minutes 26–30: Decide how to measure success

Choose a metric linked to the problem. For example, track the percentage of applicable encounters with authorization documented before service or the number of repeat authorization-related denials.

Review the result at a future meeting.

Thirty minutes of structured learning can be more valuable than hours of uncoordinated rework.

The purpose is not to blame staff. It is to make recurring problems visible and prevent them where possible.


Metrics: Track What Changes Your Next Decision

Start small. A practice may begin with five measures:

  1. First-pass acceptance rate: The proportion of claims accepted on the first submission, using a consistent definition.
  2. Preventable rejection rate: The share of claims rejected for issues the practice could reasonably prevent.
  3. Denial rate: The proportion of claims denied, distinguishing initial and final outcomes where the data allow.
  4. Accounts receivable aging: How long balances remain outstanding and why they are unresolved.
  5. Rework burden: Staff time spent correcting errors, checking status, obtaining missing information, and resubmitting claims.

A commonly discussed first-pass acceptance target is 95% or higher, but it is not a universal standard. Establish your own baseline and compare it with appropriate specialty and payer benchmarks.

Do not measure performance without context. Claim volume, payer mix, contractual terms, and external policy changes can affect results.

Each metric should have a definition, an owner, and a clear purpose.

If a metric changes but nobody knows what action to take, the dashboard has reported a fact—not delivered an insight.


Pitfalls: How Good Improvement Projects Become More Administrative Work

Buying before diagnosing. A product may be impressive but irrelevant to the actual failure. Identify the problem before choosing the tool.

Tracking everything. Too many metrics can obscure the most important signals. Start with the measures that support decisions.

Fixing the claim but not the process. Correct the immediate error, then investigate whether a repeatable change could prevent recurrence.

Assuming every denial is preventable. Some problems involve payer decisions, coverage limitations, contractual disputes, or circumstances outside the practice's control. Separate controllable causes from external ones.

Automating without oversight. Rules can be outdated, systems can be misconfigured, and AI can be wrong. Establish review and exception handling.

Ignoring staff experience. Front-desk employees and billers often know where the workflow breaks down. Include them in the improvement process.

Confusing activity with completion. A request sent is not necessarily received. A claim submitted is not necessarily paid. A task marked complete is not necessarily resolved.

The goal is not to make everyone busier at the same task. It is to reduce the number of tasks that exist only because an earlier step failed.


Tools and Resources: Use What You Already Have Before Buying More

Review your existing EHR, practice management system, clearinghouse, and payer portals.

Depending on your system and contract, useful capabilities may include eligibility verification, claim validation, electronic submission, rejection tracking, authorization work queues, remittance processing, and aging reports.

Before purchasing a new product, ask:

  • What specific problem will it solve?
  • Can an existing system already address the problem?
  • What will implementation, integration, training, and maintenance cost?
  • How will staff know when the automation is wrong?
  • Who will own exceptions and ongoing rule updates?
  • What measurable result would justify the investment?

AI can help identify recurring patterns, flag missing information, prioritize work, and summarize issues. But its value depends on the accuracy of the source data, the relevance of its recommendations, and appropriate human oversight.

Buy technology to solve a problem—not to avoid having to define one.


Legal, Practical, and Ethical Considerations

Revenue cycle improvement must protect patients and the practice.

Privacy and security: Billing systems may contain protected health information. Review access controls, vendor arrangements, data handling, audit trails, and applicable HIPAA obligations before introducing a new service.

Accurate coding and documentation: Claims should reflect the services actually provided and the supporting record. Automation should not be used to justify unsupported codes or inaccurate documentation.

Payer requirements and deadlines: Authorization, coverage, filing, and appeal rules vary. Use current, applicable guidance rather than relying solely on old spreadsheets or informal recollection.

Human accountability: Define who reviews consequential AI recommendations, resolves exceptions, and corrects errors.

Ethical automation: The objective is accurate reimbursement and reduced administrative friction—not billing more regardless of clinical appropriateness.

Efficiency is valuable only when it preserves accuracy, security, fairness, and accountability.


The Business Case: Calculate the Cost of Rework

Do not assume that a new tool will deliver a promised return.

Start by estimating four things:

  • Staff hours spent on recurring corrections.
  • Delays between service, submission, adjudication, and payment.
  • Financial exposure from underpayment, unresolved claims, and missed deadlines.
  • The full cost of the proposed improvement.

Then estimate what proportion of the problem the practice can realistically prevent.

Do not assume that every hour saved translates into direct cash savings. Staff capacity may be redeployed, and some problems may persist. Likewise, a lower denial rate does not automatically prove higher net collections.

Measure operational and financial outcomes separately where possible.

The strongest business case is not a vendor's promise. It is a measurable improvement in your own practice.


Future Outlook: From Reactive Billing to Upstream Intelligence

Healthcare is moving toward more electronic exchange, connected systems, and wider use of AI.

That creates opportunities for independent practices to access capabilities that once required larger teams and more specialized infrastructure.

But the most important opportunity may not be making every existing task faster.

It may be preventing the conditions that create unnecessary tasks in the first place.

Imagine a workflow that flags missing information before the encounter is finalized, identifies an authorization requirement before the scheduled service, detects a recurring documentation gap, and shows where a handoff repeatedly fails.

Such capabilities could reduce avoidable rework when they are accurate, well integrated, and supported by clear processes.

They will not eliminate every denial. Coverage disputes, payer changes, contractual issues, and clinical judgment will remain.

The future should combine better source data, useful automation, human judgment, and clear accountability.

For a small practice, that future does not have to start with a sophisticated AI platform. It may start with a better intake question, a reliable checklist, a clearer handoff, or a weekly review.

First understand the problem. Then improve the process. Then automate where automation makes sense.

The next leap in healthcare efficiency may come not from doing more work faster, but from eliminating work that should never have existed.


Frequently Asked Questions

What is revenue cycle management?

Revenue cycle management (RCM) covers the administrative and financial process from patient registration through claim submission, adjudication, payment, and follow-up.

Why are medical claims denied?

Reasons can include coverage limitations, missing information, authorization issues, coding or documentation problems, and filing deadlines. The appropriate response depends on the specific reason and circumstances.

Can small clinics reduce denials without hiring more staff?

Often, they can begin by reviewing recurring problems, clarifying responsibilities, improving checklists, using existing system features, and tracking a few meaningful metrics. Additional staffing may still be necessary in some circumstances.

Does AI eliminate medical billing errors?

No. AI can help detect patterns and automate selected tasks, but its performance depends on data quality, configuration, integration, and oversight. It can also produce incorrect results.

What is the difference between a rejected claim and a denied claim?

A rejection generally means a claim or transaction failed a technical or administrative check before normal adjudication. A denial generally means the payer evaluated the claim or service and determined that payment would not be made in whole or in part under the applicable circumstances. Follow the relevant payer and clearinghouse definitions.

What should a small clinic measure first?

Consider first-pass acceptance, preventable rejections, denial rate, accounts receivable aging, and rework burden. Define each measure consistently and assign an owner.

What is the Five Whys technique?

It is a problem-solving method that repeatedly asks why an issue occurred to identify an underlying process weakness. Five is a guide, not a rigid requirement. Some problems have several contributing causes.

Does insurance eligibility verification guarantee payment?

No. Eligibility information does not guarantee that a specific service will be covered or paid. Other requirements may apply.

How should a practice evaluate a new billing tool?

Start with a documented problem, estimate the total cost, assess workflow fit and data requirements, define human oversight, and agree on measurable outcomes before implementation.

What is the first step a clinic owner can take tomorrow?

Review a small sample of recent denials and rejections. Choose one recurring issue and trace it to the earliest point where the practice could have detected or prevented it.


Final Thoughts: Stop Paying for the Same Mistake Twice

Physicians and clinic owners already have enough competing demands. They should not have to build an enterprise-sized operation just to identify recurring administrative failures.

Better billing may require better software. Sometimes it requires more staff. Often, it also requires something less glamorous: clearer information, reliable handoffs, and a process that learns from its own mistakes.

The goal is not to eliminate every denial or automate every task.

The goal is to make fewer preventable mistakes, spend less time correcting them, and protect the time healthcare teams need for patients.

Find the starting point. Fix one preventable gap. Measure whether it worked.


Call to Action: Help Rethink Medical Billing

What if the biggest opportunity in medical billing is not processing claims faster, but preventing avoidable errors before a claim is created?

Share your perspective in the comments: Where does your practice lose the most time—registration, eligibility, prior authorization, documentation, claim submission, or follow-up?

If this perspective resonates, consider reposting the article to help other physicians and clinic owners question the assumption that every administrative problem needs another layer of technology.

Let's move the conversation from managing rework to preventing it.


About the Author

Dr. Daniel Cham is a physician-entrepreneur and medical technology consultant focused on healthcare management, medical billing, and practical innovation in clinical operations. He is the founder of OnnX, an AI-powered medical billing SaaS for small and medium-sized physician-owned clinics.

His work explores how better data capture, clearer handoffs, and appropriate automation can help practices reduce avoidable administrative work and improve revenue cycle operations.


Disclaimer

This article provides general educational information about healthcare administration and revenue cycle management. It is not legal, medical, coding, billing, or reimbursement advice for a specific situation. Requirements vary by payer, contract, jurisdiction, and clinical circumstances. Consult qualified professionals and authoritative guidance when making decisions about compliance, claims, coding, authorization, or reimbursement.


Continue the Conversation

Explore practical insights on healthcare operations, medical technology, innovation, and the realities of running a medical practice.

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Knowledge creates possibilities. Practical insight turns possibilities into progress. Keep learning, keep questioning, and help shape a more effective healthcare system.

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References

1. Medicare Advantage Organizations Overturned Nearly All Appealed Prior Authorization Denials for Skilled Nursing Facility Admission, Raising Concerns About Initial Denials
U.S. Department of Health and Human Services, Office of Inspector General (2026)

Examines the finding that Medicare Advantage organizations overturned 95% of appealed prior-authorization denials for skilled nursing facility admission in the study. This finding applies to the specific category studied, not all Medicare Advantage denials.

2. Some Medicare Advantage Organization Denials of Prior Authorization Requests Raise Concerns About Beneficiary Access to Medically Necessary Care
U.S. Department of Health and Human Services, Office of Inspector General (2022)

Examines Medicare Advantage prior-authorization denials and concerns about whether beneficiaries received access to medically necessary care.

3. Reconsideration by the Medicare Advantage (Part C) Health Plan
Centers for Medicare & Medicaid Services (CMS)

Provides information about the Medicare Advantage reconsideration process and related appeal procedures.

4. 95% of Denied Medicare Advantage Appeals Are Reversed. So Why Are Patients Being Denied in the First Place?

Explores the implications of the 95% overturn finding for patient access, administrative burden, and independent medical practices.

5. Your Billing Denial Has a Plot Twist: The Problem May Have Started Before the Claim Existed

Explores how upstream information gaps and broken handoffs can create downstream billing problems.

6. The Best Claim Is the One You Never Have to Fix

Examines why preventing avoidable claim errors can be more valuable than simply correcting them faster.


Hashtags

#MedicalBilling #RevenueCycleManagement #HealthcareAdministration #MedicalPracticeManagement #PhysicianEntrepreneur #HealthcareAI #MedicalBillingAutomation #ClaimsManagement #PriorAuthorization #IndependentPractice #HealthcareInnovation #DataQuality #PracticeEfficiency #OnnX

 

Tuesday, October 27, 2026

Your Billing Denial Has a Plot Twist: The Problem May Have Started Before the Claim Existed

You can’t fix a leak by mopping the floor. Medical billing’s most expensive mistakes may begin long before a claim is submitted—when critical information is missed, misplaced, or lost between systems.



“Prior authorization won’t be fixed by technology alone. It requires the entire healthcare system to work together to solve real-world challenges.” — Dr. Mehmet Oz, CMS Administrator, May 13, 2026


The claim went out. The denial came back. Everybody blamed billing.

Picture a familiar scene.

A physician sees a patient. The visit goes well. The documentation is completed. The billing team submits the claim.

Then comes the plot twist.

The claim is denied because authorization was missing, coverage information was incorrect, documentation didn’t support the service, or a required detail never made it into the workflow.

Now someone must investigate, call the payer, open a portal, search the chart, ask the clinical team for clarification, correct the information, resubmit the claim, and wait again.

Congratulations. Your practice has just purchased another round of administrative work for a problem it may have been able to prevent.

And the most frustrating part?

The denial may not have started in billing at all.

The error could have entered the process days earlier—during scheduling, registration, eligibility verification, referral intake, authorization, documentation, or a handoff between departments.

By the time the claim reaches the billing team, the original mistake may be hiding behind several layers of software and human workarounds.

Billing gets the blame because billing sees the failure.

But seeing where a problem becomes visible is not the same as identifying where it began.


The contrarian take: Your billing team may not be the problem

Healthcare has become remarkably good at building systems to clean up after other systems.

One team collects patient information. Another verifies coverage. Someone else checks authorization. A clinician documents the encounter. A coder interprets the record. A biller translates the encounter into a claim. A clearinghouse checks the submission. A payer applies its rules.

Each participant may be doing their job correctly.

Yet the overall process can still fail.

Why?

Because a workflow is only as reliable as the information that survives every handoff.

A patient’s insurance changes. A referral arrives late. An authorization is associated with the wrong service. A clinical note lacks a necessary detail. A scheduling change never reaches the person responsible for verification.

The system keeps moving, but the information underneath it has changed.

Then the practice spends time reconciling the difference.

This is the uncomfortable question healthcare operations should be asking:

How much of the work we call “billing complexity” is actually the cost of information that was incomplete, inconsistent, outdated, or disconnected upstream?

Not every denial is preventable. Payer policies, medical-necessity determinations, contract disputes, and legitimate coverage limitations are real. But treating every denial as an isolated billing event hides an important opportunity: identify the failures that repeatedly create avoidable rework.

A faster denial-management process may improve recovery.

Preventing the underlying error may eliminate the need for recovery in the first place.


The numbers make this more than an administrative annoyance

The Centers for Medicare & Medicaid Services (CMS) estimates that requesting prior authorizations costs providers approximately $20–$50 per hour and takes an average of 13 hours per week. CMS translates that into roughly $34,000 and 700 hours of administrative time annually per provider.

 

Those estimates concern prior authorization, not all medical billing denials. They should not be interpreted as the cost of every denial or as a guaranteed savings opportunity for every practice. But they illustrate the scale of the administrative burden surrounding information collection, submission, follow-up, and coordination.

Now consider what happens when authorization work is disconnected from scheduling, the clinical record, or insurance verification.

The practice may not simply spend time obtaining an approval. It may also spend time discovering that an approval was needed, locating missing documentation, correcting an order, rescheduling a patient, and explaining the delay.

One information gap can generate several downstream tasks.

The real cost is not always the individual error. It is the chain reaction the error creates.

A current signal from the industry: Digital connections are not enough

In an interview published by Healthcare Dive on October 26, 2026, Ryan Bohochik, Epic’s vice president of value-based care, described the outdated ways healthcare organizations still exchange information:

“There’s just all these ways that people have to exchange information that feel outdated.”

 

That observation matters because digitizing an old process does not automatically make the process intelligent.

A fax can become a scanned PDF. A phone call can become a portal message. A spreadsheet can become a dashboard. A manual checklist can become an electronic form.

But if the underlying information is still incomplete, duplicated, or disconnected, the same problem can travel through a more modern interface.

Technology can accelerate the exchange of information. It cannot reliably compensate for information that was never captured, was captured incorrectly, or cannot be matched to the right patient, service, payer, and workflow.

The next step is not simply to make healthcare paperwork digital.

It is to make the information dependable enough that fewer people have to chase it.

The 2027 shift: Why practices should pay attention now

CMS has established requirements for certain impacted health plans to implement standardized electronic prior authorization APIs beginning January 1, 2027. These interfaces are intended to support electronic exchanges involving coverage requirements, documentation, and authorization requests.

 

This is meaningful progress. Standardized interfaces can reduce portal switching, manual submission, and unnecessary back-and-forth when the participating systems are connected and the workflows are implemented effectively.

But there is a catch.

An API can transmit the information it receives. It cannot magically guarantee that the information is correct, complete, current, or clinically appropriate.

A connected system can still send bad data efficiently.

A well-designed digital workflow therefore needs more than connectivity. It needs reliable information capture, clear ownership, validation, context preservation, and a way to identify missing requirements before they become expensive problems.

For independent practices, the practical question is not just, “Will our EHR support the new technology?”

It is also:

“Will our workflow be ready to use it correctly?”


Six places where a billing problem can begin before billing

1. Patient registration

A transposed digit, incorrect demographic detail, or mismatched insurance record can follow the patient through multiple systems.

What to do: Establish a consistent registration checklist. Validate key fields at intake and define when information must be rechecked rather than assuming that yesterday’s record is still accurate.

2. Insurance eligibility

An insurance card is evidence of coverage information—not a guarantee that a particular service is covered on a particular date under the applicable plan rules.

What to do: Verify eligibility at the appropriate point in the workflow. Record the verification date, payer response, relevant coverage details, and any unresolved discrepancies.

3. Referral and prior authorization

A service may require a referral, authorization, or specific supporting documentation. Requirements can vary by payer, plan, service, and clinical circumstances.

What to do: Define who checks requirements, who submits the request, who tracks the decision, and who confirms that the approval applies to the intended patient, service, provider, and date range.

4. Clinical documentation

A clinician may have provided appropriate care, yet the record may not contain all the information needed to support coding, medical necessity, or payer-specific requirements.

What to do: Use clinically appropriate documentation guidance and targeted prompts. Do not turn every note into a giant checklist or encourage documentation that is not supported by the actual encounter.

5. Handoffs between teams

The front desk knows one thing. The authorization team knows another. The clinician updates the plan. The billing team receives an incomplete snapshot.

What to do: Identify the information that must follow the patient and encounter from one stage to the next. Assign clear ownership for updates and exceptions.

6. Claim submission and feedback

Some issues can only be identified after a claim is submitted or a payer responds. The mistake is not necessarily that the practice failed to prevent every error. It is failing to learn from recurring errors.

What to do: Categorize denials by root cause, source workflow, payer, service type, and responsible process—not merely by the team that handled the final claim.

The goal is to distinguish a one-time exception from a repeatable failure.


A practical playbook: Stop managing denials as disconnected events

You do not need to replace every system in your practice to begin improving the process. Start by examining how information moves through the systems you already have.

Step 1: Establish your baseline

Review a meaningful sample of recent denials, rejections, authorization delays, and claims requiring rework.

For each case, ask:

  • What was the immediate reason for the failure?
  • When did the underlying information first become incorrect or incomplete?
  • Which workflow owned that information at the time?
  • Could the problem reasonably have been detected earlier?
  • Was the information available but ignored, unavailable, outdated, or never collected?

Avoid assuming that every denial has a single root cause. Some cases involve multiple contributing factors.

Step 2: Separate the symptom from the cause

A denial code tells you something about the payer’s response. It does not always explain the full operational history that led to the response.

For example, an authorization-related denial may involve a missed requirement, a delayed request, a mismatch in the authorization record, a change in the scheduled service, or a payer-processing issue.

Build a root-cause category for each recurring failure. Keep payer adjudication reasons separate from your internal operational findings.

Step 3: Map the information journey

Follow the process from appointment scheduling to claim submission.

Document where patient, insurance, referral, authorization, clinical, and service information is created, checked, updated, and transferred.

Look for duplicate entry, manual rekeying, disconnected portals, unclear ownership, and information that becomes stale without anyone being notified.

A simple workflow map often reveals more than another meeting about “improving efficiency.”

Step 4: Create checks at the point of risk

A useful control happens before the error becomes expensive.

Examples include a missing-information alert before authorization submission, a verification checkpoint when insurance changes, or a review of whether the authorization matches the planned service.

Use proportionate checks. If every routine task triggers an alert, staff will learn to ignore alerts—including the important ones.

Step 5: Assign an owner to every exception

Automation should not create a new mystery about who is responsible.

Define who reviews a mismatch, who contacts the payer, who obtains missing documentation, who updates the record, and who confirms resolution.

Escalation rules should make exceptions visible rather than allowing them to disappear into an inbox.

Step 6: Close the learning loop

Review recurring failures regularly. When a root cause is confirmed, fix the workflow, update the relevant guidance, train the people involved, and monitor whether the failure rate changes.

A denial resolved today is useful.

A recurring denial prevented next month is better.


Measure the process—not just the money recovered

A practice can improve its denial-recovery numbers while continuing to generate unnecessary administrative work. Recovery and prevention are different objectives, so measure both.

Consider tracking:

  • Initial denial rate: The share of submitted claims denied initially, with a clearly defined denominator and reporting period.
  • Clean-claim rate: The share of claims accepted without preventable correction or rework, using a consistent definition.
  • Authorization-related failure rate: The frequency of failures associated with missing, mismatched, expired, or otherwise problematic authorizations.
  • Rework hours: Staff time spent correcting information, researching failures, and resubmitting claims.
  • Time to resolution: The elapsed time from identifying a problem to a documented resolution.
  • Repeat-cause frequency: How often the same root cause returns after a corrective action.
  • Avoidable appointment disruption: Cancellations or rescheduling attributable to preventable administrative issues.

Break results down by payer, service, workflow, and root cause where the data supports it. Establish a baseline before declaring improvement.

Do not claim that an intervention saved money simply because denial rates declined during the same period. Changes in payer mix, service mix, coding practices, staffing, and other factors can affect results.

If you only measure how quickly your team cleans up mistakes, you may never discover why the same mistakes keep arriving.


Five traps that make the problem worse

Trap 1: Buying another tool before understanding the workflow.
Software may help, but automating a poorly understood process can preserve the same failure at greater speed.

Trap 2: Blaming the last person who touched the claim.
The person who discovers the problem may have had no control over where it originated.

Trap 3: Treating every denial as preventable.
Some denials are legitimate disputes or reflect payer decisions outside the practice’s control. Prevention requires accurate classification, not wishful thinking.

Trap 4: Adding endless alerts.
Too many alerts create fatigue and encourage workarounds. Prioritize checks based on risk and clinical or financial significance.

Trap 5: Using AI without accountability.
An AI system can help extract information, flag inconsistencies, summarize payer requirements, or route exceptions. It can also misread a document, rely on stale information, or generate a confident but incorrect recommendation. Human oversight, validation, auditability, privacy protections, and clear escalation paths remain essential.

The objective is not to make staff follow more steps forever. It is to remove avoidable steps while protecting the quality and integrity of the work.


Myth buster: “If we have an EHR, our information problem is solved.”

Not necessarily.

An electronic health record can store large amounts of information and still leave gaps between clinical documentation, scheduling, insurance verification, authorization, and billing.

Data can exist in the system without being structured correctly for a particular workflow. It can be available but outdated. It can be accurate in one place and inconsistent in another.

The question is not simply whether information is electronic.

The question is whether the right information reaches the right person or system, at the right time, in a usable form—with enough context to support the next decision.

That is a workflow and information-quality challenge, not just a software-storage challenge.


Where AI actually belongs

AI can be useful in identifying missing fields, comparing records, extracting information from documents, flagging inconsistencies, summarizing authorization requirements, and prioritizing exceptions for human review.

But AI should not be treated as a magic denial eraser.

If the source data is incomplete, the system needs to recognize uncertainty rather than quietly manufacture an answer. If payer requirements change, the workflow needs a way to use current, authoritative information. If an AI-generated suggestion affects a clinical or financial decision, the practice needs appropriate review and accountability.

The best use of AI is not necessarily to replace a person at the end of the process.

It may be to give that person a cleaner, more complete, and more reliable starting point.

The goal should be less administrative detective work—not a faster detective.


The bigger idea: Build a system that learns upstream

Imagine a practice that does more than record a denial and send it to someone for follow-up.

It identifies the failure pattern, traces the problem to the earliest reliable evidence, assigns responsibility for the correction, and changes the workflow so the same error is less likely to recur.

That requires a continuous information process:

Capture → Structure → Preserve → Propagate → Act → Learn

  • Capture the relevant information at the point it becomes available.
  • Structure it so that people and systems can interpret it consistently.
  • Preserve the context that gives the information meaning.
  • Propagate updates to the workflows that depend on them.
  • Act when a required element is missing, inconsistent, or outdated.
  • Learn from the outcome and improve the process.

This is the difference between processing a claim and improving the conditions that make a clean claim possible.

It is also a more useful way to think about healthcare AI: not merely as a tool that handles paperwork faster, but as an intelligence layer that helps practices detect and resolve information problems earlier.

That is the direction we are exploring with ONNX 2.0—upstream healthcare revenue-cycle intelligence designed around the realities of physician-owned practices and the people who keep them running.

The point is not that every billing problem can be eliminated. It cannot.

The point is that practices should not have to accept preventable information failures as the normal price of doing business.


Frequently asked questions

1. Are most medical billing denials caused by front-end mistakes?

Not necessarily. Denials can arise from eligibility, authorization, documentation, coding, medical necessity, payer policy, contract terms, processing errors, and other causes. Practices should analyze their own denial data before deciding which causes deserve priority.

2. Should a practice focus on denial prevention or denial management?

Both. Denial management is necessary for failures that occur, including legitimate disputes. Prevention targets recurring, avoidable causes before they create rework. A mature process measures each separately.

3. Can AI prevent medical billing denials?

AI may help detect missing information, inconsistencies, and workflow risks, but it cannot guarantee that claims will be paid. Outcomes depend on data quality, payer rules, implementation, clinical context, and appropriate human review.

4. Will electronic prior authorization solve the problem?

It can reduce certain manual steps and improve information exchange. It will not automatically correct inaccurate source data, resolve every payer-policy dispute, or ensure that disconnected workflows operate as one process.

5. Where should an independent practice begin?

Start with a sample of recent denials and rework cases. Identify recurring causes, trace them upstream, map the relevant handoffs, and implement one measurable improvement. Expand after confirming that the change works.


The question healthcare should be asking

We have invested years in getting claims submitted, tracking denials, automating follow-up, and speeding up recovery.

Those efforts matter.

But what if the next major improvement in medical billing does not come from getting better at fixing claims after they fail?

What if it comes from making fewer avoidable failures reach the claim in the first place?

If your practice could eliminate one recurring administrative failure before it reached billing, which would you choose—and where would you look for its true source?

Share your experience in the comments. If this perspective challenges how you think about revenue-cycle management, repost it so more practice leaders can join the conversation.


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 practical insights for professionals navigating complex challenges at the intersection of healthcare, technology, and medical practice.

He is the founder of ONNX 2.0, an initiative focused on upstream healthcare revenue-cycle intelligence and reducing avoidable administrative and billing problems.

Connect with Dr. Cham on LinkedIn.

Free resource: A free resource is available in the Featured section of my LinkedIn profile—no signup required.

Continue the conversation

Connect: LinkedIn | Website | YouTube


Disclaimer

This article is for general educational and informational purposes only. It does not constitute medical, legal, coding, reimbursement, compliance, or financial advice. Requirements vary by payer, plan, service, jurisdiction, and individual circumstances. Practices should consult qualified professionals and authoritative current guidance before changing clinical, billing, authorization, or compliance workflows.


References

1. The Hidden Cost of Medical Billing: Why Clinics Are Losing Revenue in Silence — Explores how fragmented workflows and administrative inefficiencies contribute to revenue leakage in medical practices.

2. Healthcare Saved a Stranger’s Life in Minutes. Why Does It Take Months to Fix a Broken Claim? — Examines the contrast between rapid clinical action and slow administrative processes, reinforcing the case for preventing recurring billing problems.

3. Unpacking the Medical Billing Process: From Visit to Payment — Explains the medical billing lifecycle and provides context for how upstream information gaps can affect downstream reimbursement.

4. CMS: Electronic Prior Authorization — Provides official information on prior authorization burden, estimated provider time and costs, and electronic authorization requirements.

5. Healthcare Dive: Prior Authorization Takeaways from Epic Ahead of the Looming API Deadline — Discusses healthcare information-exchange challenges and industry efforts to improve prior authorization through digital connectivity and automation.

6. CMS: Moving Prior Authorization into the 21st Century — Outlines CMS’s efforts to modernize prior authorization and reduce administrative friction in healthcare.

 

#MedicalBilling #RevenueCycleManagement #HealthcareAI #PhysicianEntrepreneurship #MedicalPracticeManagement #PriorAuthorization #HealthcareOperations #ONNX2

 

Monday, October 26, 2026

The Best Claim Is the One You Never Have to Fix

The most expensive mistake is the one you could have prevented. Healthcare keeps buying smarter tools to fight denials—but the real breakthrough may be stopping preventable errors before they become claims, rework, and lost revenue.


“On our current trajectory, AI is set to exacerbate existing system failures.”
— Caroline Pearson, executive director of the Peterson Health Technology Institute

 

A faster way to fix a preventable mistake is still a way to fix a preventable mistake.

Imagine a small physician-owned clinic on an ordinary Tuesday.

A patient arrives. The front desk has checked eligibility. The authorization has supposedly been handled. The physician provides the service. The documentation is completed. The biller submits the claim.

Then the denial arrives.

The authorization number is missing. The approved service does not match the service performed. Or a payer requires documentation that never reached the right place.

Now the office becomes a detective agency.

The biller searches the system. The practice manager calls the payer. Someone asks the physician to clarify a note written weeks ago. Another employee resubmits the claim. Everyone waits.

Eventually, the claim may get paid.

The team celebrates—not because the process worked, but because it recovered from the process failing.

Healthcare has become remarkably good at cleaning up messes. Why are we so reluctant to ask who keeps making them?

Nobody deliberately schedules rework. Nobody hires a medical biller because they love chasing missing information. And no physician opens a clinic hoping to spend the afternoon explaining an authorization that should have been documented correctly in the first place.

Yet many practices spend valuable staff hours correcting problems that began before billing ever touched the claim.

It is the administrative equivalent of mopping the floor while the faucet is still running.

The mop may be excellent. The problem is upstream.


The industry may be solving the wrong half of the problem

Healthcare has invested heavily in revenue-cycle management, coding tools, denial analytics, automated appeals, and increasingly sophisticated AI.

These tools can be valuable. Claims still need to be processed accurately. Denials still need to be investigated. Payers still make decisions that require follow-up and, sometimes, appeals.

But a question deserves more attention:

What if the biggest opportunity is not fixing claims faster, but preventing avoidable problems from reaching the claims department at all?

Consider three common situations.

An authorization exists, but the information is not available to the person preparing the claim.

A payer requires a particular document, but nobody identifies the missing item until the claim is denied.

A clinical record contains the relevant information, but it does not flow reliably into the next operational step.

In each case, the information may exist somewhere. The failure occurs because it is incomplete, inconsistent, inaccessible, or disconnected from the action that depends on it.

This is why I believe healthcare billing is not merely a tooling problem. It is also an information-quality problem.

A new tool may help a team work around that problem. A better-designed process may prevent it from recurring.

The distinction is important because an organization can automate a bad workflow without improving the workflow itself.

Imagine hiring a robot to run in circles. The robot may run faster, never complain, and produce an impressive activity report.

It will still be running in circles.

Three stages. One expensive chain reaction.

Revenue-cycle management is often divided into three broad stages.

The front end: Registration, eligibility, benefits, referrals, and prior authorization.

The middle: Clinical documentation, coding, charge capture, and claim preparation.

The back end: Claim submission, payment posting, denial management, appeals, and collections.

The problem is that these stages depend on one another, while the information connecting them may not be dependable.

A demographic discrepancy can affect eligibility. A missing authorization detail can delay reimbursement. Incomplete documentation can create coding questions. A payer-specific requirement can be overlooked. A charge can fail to match the documented service.

Each problem looks like a separate task when it reaches the employee responsible for fixing it.

But the tasks may share a common cause: information did not arrive at the right place, in the right form, at the right time.

The final person to touch an error is not necessarily the person—or process—that caused it.

That is why blaming the billing department for every denial is not just unfair. It can prevent the organization from finding the actual failure.

If the same issue appears repeatedly, the right question is not simply, “Who needs to work harder?”

It is, “Why does this issue keep surviving our process?”

The latest evidence: more technology does not automatically mean less work

Recent developments offer a useful warning—and an opportunity.


Prior authorization: a deadline is not the same as a solution

In a September 2026 poll, the Medical Group Management Association reported that 44% of responding medical group leaders said prior-authorization turnaround had become slower in 2026 than in 2025. Only 7% said it had become faster. The poll included 178 applicable responses.

The federal decision-time requirements introduced in 2026 were intended to improve timeliness for certain impacted payers and services. Yet the practice's workload extends beyond the formal decision window.

Staff may need to determine whether authorization is required, gather documentation, locate the correct submission channel, check status, respond to requests for additional information, and follow up on unresolved cases.

A payer's decision clock does not measure every minute a clinic spends getting that decision.

A shorter deadline does not necessarily create a shorter workflow.

For practice owners, the lesson is to measure the entire process—not just the time between submission and the payer's response.


Oracle Health: AI is moving upstream

In September 2026, Oracle Health announced five planned AI capabilities across revenue-cycle workflows, including prior authorization, documentation quality, charge capture, professional coding, and appeals management.

The announcement reflects a broader shift: using technology to identify reimbursement problems earlier, rather than concentrating exclusively on downstream correction.

That is a promising direction. But an announcement is not proof of a realized financial outcome. Practices still need to evaluate availability, accuracy, integration, implementation costs, and measurable results.

For a small practice, the useful question is not, “Does this product have AI?”

Ask instead: Which recurring problem will it prevent, and how will we prove that it worked?


The warning from Peterson Health Technology Institute

Caroline Pearson's October 2026 analysis argues that AI's potential depends on better evidence, appropriate incentives, safeguards, and redesigned workflows.

The warning is not that AI has no value. It is that technology can reinforce existing system failures if organizations automate activity without improving outcomes.

That should resonate with every independent practice considering a new platform.

If a tool generates more alerts, more documentation, more reviews, and more appeals—but does not reduce the total burden, the practice may have purchased a more sophisticated way to remain busy.

Automation is an amplifier. It does not decide which problem deserves to be amplified.


The practical answer: move the intervention closer to the source

Prevention does not mean eliminating every denial. Some denials arise from payer decisions, coverage limitations, contract disputes, or circumstances outside the practice's control.

It means identifying the problems that can be prevented and addressing them earlier.

Here is a practical approach for a small or medium-sized physician-owned practice.

First, choose one recurring failure. Do not start by trying to transform the entire revenue cycle. Select a problem that occurs frequently, consumes staff time, or delays payment.

Second, establish a baseline. Record how often it happens, why it happens, how much staff time it consumes, and what financial impact follows.

Third, trace the problem backward. Was the information missing? Was it incorrect? Did it exist but fail to reach the next person? Was the requirement misunderstood? Was a check performed too late?

Fourth, add a control at the earliest practical point. This might be a pre-visit verification, a required-field check, an authorization-to-service match, a documentation checklist, or a clear exception-routing process.

Fifth, test whether the change works. Compare performance with the baseline. Look at rework, staff time, claim acceptance, denial reasons, and total cost.

Sixth, improve the process before expanding it. If the same failure continues, determine whether the control is poorly designed, the data are unreliable, or the root cause has not been addressed.

The point is not to add more checkpoints until staff need a map to get through the day.

The point is to put the right checkpoint in the right place.


Where AI actually helps

AI may help identify missing information, compare data across documents, summarize payer requirements, flag inconsistencies, and direct exceptions to the appropriate person.

But AI should support accurate work—not manufacture certainty.

A useful system should be able to show which information supports its recommendation, identify conflicts, acknowledge uncertainty, and route ambiguous cases for human review.

It should not invent clinical documentation, recommend unsupported coding, or treat an authorization as a guarantee of payment.

Before adopting a tool, ask the vendor:

  • Can it explain why it flagged a problem?
  • Can it identify the source information behind its recommendation?
  • What happens when information is missing or contradictory?
  • Can staff override an incorrect recommendation and document why?
  • Does it integrate with the systems the practice already uses?
  • How does the product perform across different payers and service types?
  • What are the total implementation, training, maintenance, and review costs?
  • How will we measure whether the tool improves outcomes?

Do not buy a promise of efficiency. Buy a measurable improvement in a defined problem.

A system that saves five minutes on one task but adds ten minutes of review elsewhere may not be an improvement. A system that reduces avoidable errors but creates excessive false alerts may not be an improvement either.

The relevant measure is the total effect on the workflow.


The metrics that matter more than a flashy dashboard

A polished dashboard can make almost any process look impressive. Practice owners need measures that reflect operational reality.

Track:

  • First-pass claim acceptance: How often claims are accepted without correction or resubmission.
  • Avoidable denial rate: How many denials arise from problems the practice could reasonably prevent.
  • Rework hours: How much staff time goes toward investigating, correcting, resubmitting, and appealing claims.
  • Days in accounts receivable: How long it takes to collect payment.
  • Aged receivables: How much outstanding revenue remains unpaid beyond 90 days.
  • Authorization cycle time: How long the full process takes, including preparation and follow-up.
  • Documentation and charge discrepancies: How often issues are caught before submission versus afterward.
  • AI performance: False positives, missed issues, staff overrides, and time spent reviewing recommendations.
  • Total cost of ownership: Software, implementation, integration, training, maintenance, and human oversight.

Compare like with like, and separate problems the practice can prevent from those it cannot control.

For example, a lower denial rate is not automatically a win if claims are being held too long before submission. Faster processing is not automatically a win if it creates more corrections later.

A metric is useful only when it tells you whether the system is getting better—not merely busier.


Five myths worth retiring

Myth 1: A clean claim guarantees payment.

It does not. Coverage, medical necessity, authorization, documentation, contract terms, and payer policies can still affect reimbursement.

Myth 2: More AI means fewer denials.

Not necessarily. Results depend on data quality, integration, workflow design, accuracy, and oversight.

Myth 3: Every denial is billing's fault.

No. Problems may begin in scheduling, registration, authorization, clinical documentation, payer processing, or other parts of the system.

Myth 4: Conservative coding is always safer.

Accurate coding is the objective. Under-coding can fail to represent legitimate services, while unsupported coding can create compliance and repayment risks.

Myth 5: A secure-sounding product is a secure product.

Security depends on access controls, encryption, monitoring, vendor practices, data governance, and incident response—not marketing language.

The lesson is simple: labels do not replace evidence.


A small-practice ROI example

Suppose a practice spends 10 staff hours each month correcting one category of preventable billing errors.

At an assumed fully loaded labor cost of $30 per hour, that is $300 per month—or $3,600 per year.

If a workflow improvement reduces that rework by half, the practice saves approximately five staff hours per month, worth $150 in direct labor under these assumptions.

That is an illustration, not an industry benchmark.

The practice should also evaluate whether the change reduces delayed cash flow, avoidable write-offs, overtime, repeated patient contacts, or other measurable costs. Those benefits should be counted only when supported by actual results.

Then subtract the cost of the intervention.

If the solution costs more than the problem it solves, it may be the wrong solution—even if the demonstration is impressive.

The smartest investment is not the most advanced tool. It is the intervention that delivers a worthwhile result at a defensible cost.


The bigger opportunity: upstream intelligence

This is the problem space that interests me as a physician-founder.

I believe healthcare has an opportunity to move beyond treating billing as a series of disconnected corrections and toward treating it as a connected information process.

The sequence is straightforward in principle:

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

Capture relevant information when it becomes available. Structure it so people and systems can use it consistently. Preserve its context. Propagate it to the next workflow. Act when something important is missing or inconsistent. Learn from recurring failures so the same problem is less likely to happen again.

The hard part is making that sequence work in real practices, where information is scattered across systems, payer requirements vary, staff have limited time, and exceptions are unavoidable.

That is what I mean by upstream intelligence: identifying preventable information failures earlier and making the next correct action clearer.

OnnX is being developed around this approach to healthcare revenue intelligence, with a focus on the needs of small and medium-sized physician-owned practices.

The thesis is not that technology can eliminate every billing problem. It is that some downstream problems can be reduced by improving the information and decisions that precede them.

Medical billing AI helps fix claims. Upstream intelligence aims to prevent the problems that break them.

That proposition must be validated through real workflows, real users, measurable outcomes, and appropriate safeguards. The goal is not another dashboard that tells the practice what went wrong yesterday. The goal is to help the practice catch more preventable problems while there is still time to do something about them.


The future belongs to systems that learn before they repeat

Denial management will remain necessary. Payer requirements will change. Documentation will still require clinical judgment. Some disputes will remain unavoidable.

The goal is not a fantasy world with zero denials.

It is a system that distinguishes preventable errors from legitimate disputes, recognizes recurring patterns, and makes the correct next step easier.

The best technology may not be the system that generates the most alerts or processes the most claims. It may be the one that quietly prevents a familiar problem from returning.

No dramatic dashboard. No celebratory meeting. Just fewer errors, less rework, and more time for patients.

That may not sound as exciting as the latest AI launch. But neither does a well-functioning plumbing system. People tend to notice plumbing only when it fails.

Healthcare billing deserves a higher standard than celebrating how quickly we repair what we could have prevented.

A resolved denial pays attention to the past. A corrected workflow protects the future.

For independent practices, that distinction can mean fewer administrative distractions, more predictable operations, and a better use of limited staff time.

The real breakthrough is not making every correction faster. It is making fewer corrections necessary.


One question for physicians, practice managers, and medical billers

Are we investing too much in fixing billing failures—and too little in preventing them?

What recurring billing problem do you believe should be caught upstream, before it becomes somebody else's emergency?

Share your experience in the comments. If this reflects what you see in practice, consider reposting it to bring more physician owners, practice managers, and medical billing professionals into the discussion.


About Dr. Daniel Cham

Dr. Daniel Cham is a physician, healthcare entrepreneur, and founder of OnnX. His work explores medical billing, practice management, healthcare technology, and the opportunity to improve the quality of information moving through healthcare operations.

Disclaimer: This article is for educational and informational purposes only. It does not constitute legal, medical, coding, compliance, tax, or reimbursement advice. Requirements vary by payer, plan, service, and jurisdiction. Practices should consult qualified professionals and applicable authoritative guidance for decisions affecting their operations.

Connect: LinkedIn | Website | YouTube

P.S. A free resource is available in the LinkedIn Featured section—no signup required.


References

1. Centers for Medicare & Medicaid Services (CMS). Interoperability and Prior Authorization Final Rule (CMS-0057-F). Explains federal prior-authorization decision timeframes and requirements for impacted payers.

2. Medical Group Management Association (MGMA), September 2, 2026. Fewer Than 1 in 10 Practices See Faster Prior Auth Turnarounds in 2026. Reports survey findings on medical practices’ experience with prior-authorization turnaround times.

3. KFF, August 13, 2026. Prior Authorization Metrics Provide New Insights into Insurer Practices, but Gaps Remain. Analyzes insurer-reported authorization data, including denial rates, appeal outcomes, response times, and limitations in publicly reported metrics.

4. The Hidden Cost of Medical Billing: Why Clinics Are Losing Revenue in Silence (and How AI Is Reshaping the Middle Layer of Healthcare). Related article examining documentation quality, revenue leakage, rework, and AI-assisted billing.

5. Healthcare Saved a Stranger’s Life in Minutes. Why Does It Take Months to Fix a Broken Claim?. Connects healthcare’s ability to respond to clinical emergencies with its continuing challenges in preventing administrative failures.

6. Unpacking the Medical Billing Process: From Visit to Payment—What Experts Really Say & What You Should Be Doing Now. Provides additional context on claim denials, payer complexity, workflow inefficiencies, and revenue-cycle management.

 

#MedicalBilling #HealthcareAI #RevenueCycleManagement #PhysicianEntrepreneur #PracticeManagement #HealthcareInnovation #ClaimDenials #RevenueIntegrity #ClinicalDocumentation #PriorAuthorization #HealthTech #HealthcareOperations #UpstreamIntelligence

 

Your Medical Billing Problem May Not Be a Billing Problem: The Hidden Cost of Creating Work You Should Never Have to Do

What if the biggest opportunity in medical billing isn’t getting denials overturned—but making fewer denials necessary? “Accurate data and...