Why independent medical practices should stop automating billing chaos—and start preventing it upstream.

“The problem isn’t volume. It’s coherence.” — Thea Campbell, MBA, RHIA, FAHIMA, writing about healthcare revenue-cycle intelligence

What if your most expensive billing problem began weeks before your biller ever saw the claim?

The $100 Question Hiding Inside Every Denial

Imagine hiring someone to fix your front door.

Every morning, they arrive to repair the same broken lock. They work faster every week. They install better tools. Eventually, you give them artificial intelligence.

The repairs become spectacularly efficient.

There is just one small problem: nobody has fixed the door.

Welcome to the strange economics of healthcare administration.

A patient arrives. Insurance information is incomplete. An authorization detail is missing. Documentation doesn’t clearly support the service. A payer requirement has changed, but the relevant team never receives the update.

The claim goes out anyway.

Weeks later, a denial arrives. A medical biller investigates, gathers records, corrects the claim, drafts an appeal, follows up, and documents the result. The practice waits for payment while staff spend valuable time reconstructing information that should have been available earlier.

Then someone buys another AI tool to automate the appeal.

The technology gets faster. The original problem gets another opportunity to happen.

That is the distinction independent physicians, clinic owners, and medical billing professionals need to understand: automating the correction of a problem is not the same as preventing the problem.

AI can help with both. But the order matters.

The next competitive advantage in medical billing may not come from the practice that processes denials fastest. It may come from the practice that consistently prevents avoidable errors before claims leave the building.

The Contrarian Thesis: More Automation Is Not Always More Progress

Healthcare has a complicated relationship with administrative technology.

When a process becomes inefficient, the natural response is to automate it. If claims are denied, automate denial management. If documentation is incomplete, introduce documentation AI. If authorization requests are delayed, automate authorization workflows.

Each solution may be useful.

But there is a question we often skip:

Why is the same problem entering the system in the first place?

The revenue cycle is not a collection of isolated transactions. It is a chain of connected decisions and information.

Eligibility affects authorization. Authorization affects scheduling and service delivery. Clinical documentation supports coding. Coding informs claim submission. Claim submission interacts with payer-specific rules. Payment outcomes should inform future workflows.

When those connections break, the practice pays for the same information gap multiple times.

First, someone must locate the missing information. Then someone must correct it. Another person may need to review it. The claim may need to be resubmitted. Payment is delayed. And the same defect may affect the next patient.

This is why the distinction between downstream automation and upstream intelligence matters.

• Downstream automation helps process, correct, appeal, and recover claims after problems appear.
• Upstream intelligence helps identify missing information, inconsistencies, and known risks before they become preventable billing failures.

Neither approach makes the other obsolete. A practice will still encounter legitimate disputes, changing payer rules, and situations that require human judgment.

But if a problem can be prevented reliably before submission, correcting it afterward should not be the preferred strategy.

A faster appeal is useful.

A correct claim that never needs that appeal is usually better.

What the Latest Industry Discussion Reveals

In an October 20, 2026 article, Thea Campbell of Solventum argued that healthcare’s challenge is not simply the quantity of information but its coherence. Her discussion of revenue-cycle intelligence emphasized connecting fragmented data, identifying documentation gaps earlier, and shifting from reactive correction toward prevention.

That perspective matters because healthcare organizations already generate enormous amounts of information. Yet having data somewhere in an electronic record does not mean the right person can find it, trust it, interpret it, and act on it at the right time.

A payer requirement buried in one workflow may never reach the person preparing the claim. A correction made after a denial may never inform the next similar encounter. An authorization number may exist but remain disconnected from the service it was intended to support.

The problem is not necessarily a lack of software.

Sometimes, the problem is that the software does not preserve and propagate the right information across the work that depends on it.

The uncomfortable counterpoint: AI can create new problems, too

The Peterson Health Technology Institute raised a related concern in a report covered by Becker’s Hospital Review on October 14, 2026. Its analysis argued that, under current fee-for-service incentives, some AI-enabled documentation, coding, billing, and authorization tools could reinforce inefficient workflows and increase costs rather than reduce waste. Executive Director Caroline Pearson warned: “On our current trajectory, AI is set to exacerbate existing system failures.”

That is a warning worth taking seriously—not proof that all healthcare AI increases costs, and not an argument against automation itself.

It highlights a more important principle:

Automating a flawed process can make the flaw more productive.

If an AI tool generates documentation faster but fails to preserve accuracy, the practice has not necessarily improved. If it submits more claims without resolving recurring information gaps, it may simply move the workload to another department. If it identifies more billable details without adequate clinical and compliance safeguards, it can create additional audit and financial risk.

The objective should not be to maximize the number of tasks AI performs.

The objective should be to improve the quality of decisions, reduce preventable rework, protect appropriate reimbursement, and preserve accountability.

The Economics of Fixing Things Twice

Medical billing costs are not limited to the invoice from a billing company or the salary of an in-house biller.

They also include the less visible cost of administrative repetition.

Consider a hypothetical independent practice that handles 2,000 claims each month. Suppose 10% require additional work because of preventable information or process problems.

That means 200 claims need attention.

If each takes an average of 20 minutes to investigate and correct, the practice spends approximately 66.7 staff hours per month on this work.

If the average loaded labor cost is $35 per hour, that represents about $2,333 in monthly labor.

These figures are illustrative assumptions, not industry benchmarks. Actual costs vary by specialty, payer mix, staffing model, denial category, and complexity.

Now imagine the practice reduces those preventable cases by 25% through better intake, information validation, and workflow coordination.

That would eliminate 50 cases per month, or about 16.7 staff hours. At the assumed labor cost, the direct labor value would be roughly $583 monthly.

That calculation does not include the potential value of faster payment, reduced write-offs, fewer patient billing surprises, or less staff frustration. Nor does it account for implementation costs, false alerts, or the fact that some denials are unavoidable.

The point is not that every clinic will achieve these results.

The point is that prevention has an economic value that practices can measure rather than merely assume.

And there is a second advantage: preventing a recurring problem may eliminate future work across multiple teams, not just the person who would otherwise correct the claim.

The Upstream–Downstream Distinction

The strongest operating model combines both.

Use downstream tools to resolve problems that remain. Use upstream intelligence to learn from those problems and reduce the likelihood that the same preventable issue recurs.

A denial should not be the end of a workflow. It should be a signal that can improve the next workflow.

Six Steps Toward Upstream Billing Intelligence

A small practice does not need to replace its entire technology stack to begin. It can start with one recurring problem, a clear owner, and a measurable baseline.

1. Capture the right information

Start with the information that materially affects whether a claim can be supported and processed correctly.

Depending on the service and payer, that may include:

• Current eligibility and coverage details.
• Required authorization or referral information.
• Complete, accurate clinical documentation.
• Correct patient and provider identifiers.
• Applicable coding and billing requirements.
• Relevant payer-specific rules and supporting records.

Do not confuse collecting more data with collecting better data.

A hundred fields that nobody uses are not necessarily more valuable than ten fields that prevent a recurring failure.

The aim is to capture the right information at the right time, from an appropriate source.

2. Structure it so people and systems can use it

Information that exists only in free-text notes, email threads, scanned documents, or someone’s memory is difficult to operationalize reliably.

Where feasible, establish standardized fields, consistent terminology, clear ownership, and validation rules.

For example, a practice may require a defined authorization status before a particular service proceeds. The exact rule must reflect the payer, service, applicable exceptions, and clinical realities—not a universal assumption that every service requires prior authorization.

Structure turns information from something people search for into something a workflow can use.

3. Preserve what has already been learned

When a biller discovers that a particular payer requires additional documentation for a recurring service, the insight should not disappear when the case is closed.

Record the finding, its source, the date it was confirmed, its scope, and who is responsible for validating future changes.

A past denial can inform a future check. But it should not automatically become a permanent rule: payer policies change, exceptions exist, and historical patterns can be misleading.

The objective is reusable knowledge, not institutional folklore disguised as an algorithm.

4. Propagate the information to the next decision

This is where many organizations lose value.

A correct authorization detail is not enough if scheduling cannot see it. A documentation requirement is not enough if the relevant clinical or billing team never receives it. A denial insight is not enough if it never reaches the workflow that created the error.

Map the handoffs.

Ask who needs the information, when they need it, what action it should trigger, and how the practice will know that the action occurred.

5. Act before the problem becomes expensive

Use rules-based validation where straightforward checks are sufficient. Consider AI when information is unstructured, patterns are complex, or context must be synthesized across sources.

Examples include flagging missing required fields, identifying inconsistencies, prioritizing exceptions, or surfacing a known documentation risk before submission.

Not every problem requires an AI model. A deterministic rule may be cheaper, easier to explain, and more reliable for a clearly defined requirement.

AI should earn its place by improving a real workflow—not by appearing in the product description.

6. Learn from outcomes

Track what happened after the intervention.

Was the claim accepted? Was the alert correct? Did staff resolve the issue? Did the same defect recur? Did the workflow create unnecessary interruptions? Did a change improve compliance without slowing care?

Feed verified lessons back into the process.

This creates a practical learning loop:

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

That sequence is not a promise that every error can be eliminated. It is a disciplined way to turn recurring administrative problems into opportunities for improvement.

What Should a Physician-Owned Practice Measure?

If a vendor promises that its AI will “transform the revenue cycle,” ask for the baseline, the denominator, the measurement period, and the operational definition.

Start with these metrics:

1. Preventable denial rate. Track the proportion of claims denied for clearly defined, addressable causes. Separate these from medical-necessity disputes, payer policy conflicts, and other categories that may not be preventable at the point of capture.

2. First-pass acceptance rate. Measure how often claims are accepted on initial submission. Define acceptance consistently; acceptance for processing is not the same as final payment.

3. Rework hours per 1,000 claims. This helps reveal administrative effort that headline denial rates can hide.

4. Days to payment. Monitor whether cleaner submissions and faster resolution are associated with shorter payment cycles. Account for payer mix and other factors that affect timing.

5. Avoidable write-offs. Distinguish preventable losses from legitimate contractual adjustments, noncovered services, and other appropriate write-offs.

6. Alert precision. Determine how often the system’s warnings identify genuine, actionable problems. Too many false alerts can undermine adoption and create alert fatigue.

7. Cost per resolved problem. Include software, staff time, vendor fees, implementation, maintenance, and oversight—not just the cost of processing a task.

8. Compliance and patient impact. Monitor documentation accuracy, audit findings, patient billing complaints, and inappropriate delays in care.

Establish a baseline before introducing a new tool. Compare similar periods and claim populations where possible. Avoid crediting the technology for changes that may have resulted from staffing, payer behavior, seasonality, or other process improvements.

The question is not simply whether AI saved time.

It is whether the practice achieved a meaningful, sustainable improvement without creating a new problem somewhere else.

Five Mistakes That Can Turn Prevention Into Another Expense

Mistake 1: Automating before understanding the workflow. If nobody can explain why a claim repeatedly fails, automation may reproduce the same failure at scale.

Mistake 2: Treating historical patterns as permanent payer rules. Historical data can reveal useful signals, but current policies and documented requirements still matter.

Mistake 3: Assuming every denial is preventable. Some denials involve disputed medical necessity, changing policies, contractual disagreements, or facts that could not reasonably have been known beforehand.

Mistake 4: Removing experienced billers from the decision process. Medical billing professionals carry context that may never be fully represented in a dataset. Use technology to support that expertise, not erase it.

Mistake 5: Measuring activity instead of outcomes. Faster claim submission, more automated tasks, and more alerts do not automatically mean fewer errors, better collections, or lower total costs.

The most dangerous implementation is one that produces impressive dashboards while leaving the underlying process unchanged.

Five Myths About AI and Medical Billing

Myth 1: More AI means fewer denials.

Reality: Results depend on the quality of the information, the design of the workflow, the type of denial, and whether staff can act on the system’s findings.

Myth 2: A denial is simply a billing department problem.

Reality: Some failures originate in registration, eligibility, authorization, clinical documentation, scheduling, coding, or handoffs between departments.

Myth 3: Upstream prevention eliminates the need for appeals.

Reality: Even accurate, well-prepared claims can face disputes and denials. Downstream expertise remains necessary.

Myth 4: Structured data makes human judgment unnecessary.

Reality: Standardization improves consistency, but exceptions, ambiguous requirements, and clinical context still demand professional judgment.

Myth 5: The most advanced AI is the best investment.

Reality: A simple validation rule that prevents a recurring error may deliver more value than a sophisticated model that identifies the problem only after it occurs.

Legal, Ethical, and Operational Guardrails

Prevention cannot become an excuse for overbilling, inappropriate automation, or unsafe shortcuts.

A responsible upstream intelligence program should include:

• Clinical and coding integrity: Documentation and codes must accurately reflect the services provided and the clinical record. Technology should not invent diagnoses, embellish medical necessity, or encourage unsupported coding.
• Privacy and security: Protect patient information, restrict access according to role, and evaluate vendors’ data handling, security controls, and contractual obligations.
• Human accountability: Define who reviews uncertain recommendations, approves consequential changes, handles exceptions, and remains accountable for the final decision.
• Auditability: Preserve the relevant source information, system recommendation, material changes, human decisions, and reasons for overriding or accepting a recommendation where appropriate.
• Change management: Monitor payer requirements, validate rules, document updates, and retire outdated logic.
• Fair and appropriate access: Ensure automated administrative checks do not create inappropriate barriers to clinically necessary care.

Applicable requirements depend on the workflow, data involved, payer contracts, and jurisdiction. Practices should obtain qualified compliance and legal advice when needed.

The goal is not to automate every decision.

It is to make routine decisions more reliable while ensuring that consequential or uncertain decisions receive the right level of review.

Where OnnX Fits: From Billing Automation to Upstream Intelligence

This is the problem OnnX is being developed to address.

OnnX’s thesis is straightforward: medical billing AI can fix claims, but upstream intelligence aims to prevent the problems that break them.

Built around physician and medical-billing experience, the concept is to help small and medium-sized practices identify missing information, inconsistencies, workflow risks, and recurring revenue-cycle problems earlier—before they become avoidable rework.

The ambition is not to promise that every denial will disappear, that every payer will behave predictably, or that human expertise can be replaced.

It is to improve the quality of information moving through the practice.

The proposed approach follows six stages:

• Capture: Obtain relevant clinical, operational, and payer information.
• Structure: Make the information consistent and usable.
• Preserve: Retain verified knowledge and important changes.
• Propagate: Deliver the right information to the people and workflows that need it.
• Act: Surface risks and support appropriate decisions.
• Learn: Use verified outcomes to improve future performance.

Over time, practice-specific context and validated outcomes could help the system distinguish recurring patterns from isolated events.

That is a development objective, not a claim that the product has already demonstrated a particular reduction in denials or operating costs. Those outcomes must be tested and measured.

The broader principle is what matters: the revenue cycle should not have to rediscover the same problem every time a new claim arrives.

The Future: From Reactive Revenue Recovery to Preventive Intelligence

The next phase of healthcare automation will not be determined by who can put the most AI agents into a workflow.

It will be determined by whether those agents have reliable information, useful context, appropriate authority, and measurable responsibilities.

Three developments are worth watching.

First, payer and provider intelligence will become more connected. Organizations will increasingly seek to understand payer-specific patterns and operational requirements before problems accumulate. Historical patterns will remain useful, but they must be validated against current requirements.

Second, AI will increasingly operate as a layer across workflows rather than as a collection of isolated tools. A system that connects eligibility, authorization, documentation, coding, and claim outcomes may provide more value than disconnected automation at each stage—provided the integrations are reliable and secure.

Third, accountability will become a competitive advantage. Practices will need evidence that their systems improve outcomes, preserve audit trails, protect patient information, and escalate uncertainty appropriately.

The winners will not necessarily be the organizations with the most automation.

They may be the ones that know which problems should never have reached automation in the first place.

Frequently Asked Questions

Is upstream billing intelligence the same as denial prevention software?

Not necessarily. Denial prevention software may focus on particular checks or claim risks. Upstream billing intelligence is a broader operating concept: connect relevant information, preserve verified knowledge, identify problems earlier, and use outcomes to improve the workflows that create claims.

Should a small medical practice invest in upstream tools before downstream AI?

Not automatically. Start with the practice’s most costly and frequent problems. If a denial queue is already creating significant financial exposure, downstream tools may be justified. If recurring errors originate in eligibility, authorization, or documentation, upstream interventions may offer a stronger starting point. Many practices will need both.

Can AI eliminate medical billing denials?

No. Some denials are preventable; others involve payer disputes, medical necessity, policy interpretation, or circumstances outside the practice’s control. A credible solution should specify which categories it addresses and report results against a defined baseline.

Do small practices need expensive AI platforms?

Not always. Standardized intake, reliable eligibility checks, clear authorization workflows, simple validation rules, and consistent denial-cause tracking may address important problems. Add AI where it solves a problem that simpler approaches cannot handle effectively.

Will upstream intelligence replace medical billers?

The objective should be to reduce repetitive work and improve the information available to experienced professionals. Human expertise remains important for exceptions, payer-specific context, compliance, appeals, and decisions that require judgment.

How should a practice evaluate a vendor’s claims?

Request results for comparable practices and claim types, a defined baseline, measurement periods, the denominator used, implementation costs, false-alert rates, and evidence that improvements persist. Ask what the system cannot do and how uncertain recommendations are handled.

What is the best first step?

Review a recent sample of denials and rework. Identify the recurring causes, estimate their frequency and cost, and trace each one back to the earliest point at which the problem could reasonably have been detected. Choose one high-impact, measurable workflow to improve.

The Question We Should Be Asking

Healthcare has spent years building increasingly sophisticated ways to find, correct, appeal, and recover from billing problems.

That work remains necessary.

But what if our next breakthrough is not a better way to repair the process after it fails?

What if it is a better way to stop sending the same preventable problem through the process in the first place?

If you own a medical practice or work in medical billing, where do you see the greatest waste today: correcting claims downstream, or failing to capture the right information upstream?

Share your experience in the comments. If you know a physician, practice manager, or medical biller wrestling with this problem, share this article with them. The most useful insight may come from the people doing the work every day.

For a free resource, see my LinkedIn Featured section—no signup needed.

About the Author

Dr. Daniel Cham is a physician and founder of OnnX, an early-stage initiative focused on upstream healthcare revenue intelligence. His perspective brings together medical practice experience, healthcare operations, and medical billing, with an interest in how better information and workflow design can reduce preventable administrative friction.

His work explores a simple question: How can healthcare organizations spend less time correcting avoidable problems and more time delivering care?

Connect: LinkedIn | Website | YouTube

Disclaimer

This article is for educational and informational purposes only. It is not medical, legal, coding, reimbursement, or compliance advice. Examples and financial calculations are illustrative, not guarantees of performance. Practices should verify current payer requirements and applicable laws and consult qualified professionals for decisions specific to their operations.

References and Further Reading

1. Campbell, Thea. “The next advantage in revenue cycle is context.” Solventum, October 20, 2026. Read the article.
2. Bruce, Giles. “AI scribes, billing tools drive up healthcare costs: Report.” Becker’s Hospital Review, October 14, 2026. Read the article.
3. Shryock, Todd. “Medical billing denials are rising: Can AI help health care practices get paid faster?” Medical Economics, March 6, 2026. Read the article.
4. Bala, Purnendu. “Where artificial intelligence actually helps in medical billing and revenue cycle management, and where it doesn’t.” Medical Economics, April 1, 2026. Read the article.

5. The Hidden Cost of Medical Billing: Why Clinics Are Losing Revenue in Silence (and How AI Is Reshaping the Middle Layer of Healthcare). Explore how fragmented workflows and administrative rework contribute to revenue leakage. Read the article.
6. Jenn Carbajal Asked for a Second Opinion. Healthcare Leaders Need One Too. Examine why better-connected information and intelligence matter in healthcare decision-making. Read the article.
7. Navigating the Rising Tide of Medical Claim Denials: Expert Insights and Strategies for 2025. Review practical approaches to understanding denial causes and improving revenue-cycle workflows. Read the article.

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