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.

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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.

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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.

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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?

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

 

The Cheapest Claim Is the One You Never Have to Fix

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?

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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.

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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.

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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.

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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.

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The Upstream–Downstream Distinction

Question	Downstream approach	Upstream approach
When does it act?	After a problem appears or a claim is submitted	Before or during the workflow that creates the risk
Primary purpose	Correct, appeal, recover, and resolve	Identify gaps, validate information, and prevent avoidable failures
Typical examples	Denial queues, appeal drafting, claim correction	Eligibility checks, authorization validation, documentation completeness
Useful measures	Appeal success, recovery rate, days to resolution	Preventable error rate, first-pass acceptance, rework avoided
Main limitation	The original work and delay may already have occurred	It cannot prevent every denial or replace expert judgment

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

 

#MedicalBilling #HealthcareAI #RevenueCycleManagement #PhysicianOwnedPractices #HealthcareOperations #OnnX

 

From Amritpal Virdee’s ICU Diary to Medical Billing: Why Healthcare Must Stop Losing the Plot

Healthcare can record every detail and still lose the plot. Before we buy more AI, we should ask why our systems keep forgetting what they already know.

“Is there an economic or operational impact that’s going to translate to dollars?” — Nassib Chamoun, founder and CEO of the Health Data Analytics Institute, at the HIMSS AI in Healthcare Forum

 

That is an excellent question for healthcare AI. It is also an uncomfortable question for the companies selling it.

A model can impress in a demonstration. A chatbot can produce a convincing answer. An automation platform can complete a task in seconds.

But did the technology improve the outcome? Did it eliminate unnecessary work? Did it make the process more reliable? Did the financial benefit exceed the cost of implementation and maintenance?

Or did we simply teach the existing problem to move faster?

In medical billing, that last possibility deserves more attention than it gets.

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A Man Survived a Heart Attack. Then He Had to Reconstruct His Own Story.

In December 2025, Amritpal Virdee was rushed to Midland Metropolitan University Hospital in England after suffering a life-threatening heart attack.

Doctors inserted a stent to restore blood flow to his heart. But his condition deteriorated. His heart stopped for several minutes, and he was admitted to the intensive care unit.

For a period of his recovery, much of his experience was missing from his memory.

While Amritpal was sedated, nurses documented his journey in an ICU diary. They recorded medical developments, family visits, conversations, and ordinary moments that might otherwise have disappeared.

His wife, Pam, and son, Ruben, were part of that journey. So was a visit from his brother.

During the visit, a nurse turned on the television so the brothers could watch the news and Match of the Day. The team discovered that Amritpal supported Newcastle United.

It was a small detail in an extraordinary situation.

Yet it helped preserve something a medical chart alone might not have conveyed: the person behind the patient.

When Amritpal later read the diary, he could begin to piece together what had happened while he could not remember it. He could understand not only the medical events but also the care, attention, and human moments surrounding them.

Catherine Beddowes, Senior Sister for Critical Care Follow Up Support at Midland Metropolitan University Hospital, explained that ICU diaries can preserve both medical milestones and personal details that help patients make sense of their experience.

Amritpal expressed his gratitude to the staff and doctors who cared for him.

The hospital published his story on October 23, 2026.

The lesson is not that a diary is a substitute for a medical record. It is that recording information and preserving its meaning are two different things.

Sometimes, caring for someone means remembering the things they cannot.

Now consider a very different setting.

A physician finishes seeing patients. The clinical work is done. The documentation is in the EHR. The billing team submits the claim.

Weeks later, someone discovers a problem.

An authorization number cannot be found. A payer requests additional information. A claim is denied. A staff member opens the EHR, checks the clearinghouse, searches email, logs into a payer portal, and asks a colleague whether anyone remembers what happened.

The question is familiar:

“Does anyone know what happened with this claim?”

No ICU alarm sounds. Nobody is fighting for their life. The consequences are not comparable to Amritpal’s medical emergency.

But the operational question is worth asking:

Why does healthcare repeatedly have to reconstruct information that should already be available?

In critical care, a diary helped a patient reconnect with his experience.

In medical billing, better operational memory could help a practice reconnect the dots between an encounter, the information supporting a claim, the actions already taken, and the work still required.

Different circumstances. Different stakes. One useful systems question: are we preserving information in a form that helps people understand what happened and decide what to do next?

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Healthcare Has a Memory Problem. It Just Calls It an Administrative Expense.

Imagine a highly experienced medical biller.

She knows which payer requires an additional document. She remembers which physician routinely misses a particular field. She knows where the authorization number is usually stored, which claims need attention before Friday, and which payer representative might actually answer the phone.

Her knowledge is valuable.

But how much of it exists in the system—and how much exists only in her head?

She may use an EHR, a practice management system, a clearinghouse, payer portals, spreadsheets, email, and a collection of notes.

Each tool has a purpose. The problem emerges between them.

The authorization is in one place. The appointment is in another. The claim is somewhere else. The reason a staff member made a particular decision may be buried in an email from three weeks ago.

Our biller knows how the pieces fit together because she has spent years learning the exceptions.

She is not merely processing claims. She is compensating for the architecture.

Now imagine she takes two weeks off.

Suddenly, the practice discovers that its operating manual has been walking around wearing comfortable shoes.

The system may contain most of the information. It simply does not preserve enough of the context to make that information easy to use.

This is the hidden cost of fragmented operations.

The most expensive information in a medical practice may be the information everyone assumes someone else remembers.

The consequences include repeated searches, duplicate work, delayed follow-up, unclear ownership, and avoidable errors. Some work is necessary. Payer requirements can be complex, and legitimate exceptions will always exist.

But there is a difference between doing necessary work and repeatedly rediscovering information because the system failed to make it available.

The first is part of healthcare administration.

The second is digital archaeology.

The tools are digital. The excavation is manual.

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The Contrarian Argument: More Automation Is Not Always More Progress

The usual pitch for healthcare technology is straightforward:

Find a repetitive task. Automate it. Save time.

Repeat until the practice becomes more efficient.

There is nothing wrong with this approach when the task is necessary, the automation is reliable, and the results are measurable.

The problem is the assumption underneath it: that the work being automated should exist in the first place.

Imagine a claim is denied because authorization information was not attached correctly.

One solution is to automate the process of finding the authorization number and attaching it to the claim.

That might help.

But what if the real problem is that the authorization was never connected to the relevant service? What if nobody owned the handoff? What if the information was captured in a system that the billing workflow could not access? What if the number was present but its scope or validity had not been verified?

Automating the final step might address the symptom while leaving the underlying failure untouched.

Congratulations. We have industrialized the workaround.

The same pattern appears in many business processes. Organizations add a dashboard to monitor a spreadsheet, then automate the spreadsheet, then build an alert to warn staff when the spreadsheet is wrong.

Eventually, someone asks whether the spreadsheet was solving the right problem in the first place.

Healthcare is not immune to this cycle.

The issue is not that automation is bad. It is that automation can make a poorly designed process more efficient at producing unnecessary work.

A useful principle from management thinker W. Edwards Deming is to examine the system in which people work rather than automatically blaming individuals for recurring failures.

If staff repeatedly miss the same step, training may help. But if the process hides critical information, leaves responsibility unclear, and offers no warning before submission, training alone may not solve the problem.

The person who touches the claim last is not necessarily the person—or system—that created the failure.

Before automating a task, ask three questions:

1. Is this task necessary?
2. Why does it exist?
3. What would prevent the underlying problem from recurring?

The goal is not to make people better at working around broken systems. It is to need fewer workarounds.

That is a much more interesting AI problem.

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A Claim Is Not an Isolated Transaction. It Is the Ending of a Story.

Medical billing is commonly divided into functions: eligibility verification, referral management, prior authorization, documentation, coding, claim submission, payment posting, denial management, and appeals.

Those functions matter. But treating them as isolated activities can obscure the larger picture.

A claim is the financial expression of a clinical and administrative journey.

Consider the sequence:

Patient → Insurance → Eligibility → Referral → Authorization → Encounter → Documentation → Coding → Claim → Payer → Payment → Denial or Exception → Resolution → Feedback

Every stage can create information the next stage needs.

An authorization is not just a number. It has context: patient, service, provider, payer, date, and applicable conditions.

A clinical note is not just a document. It supports the medical record and may support the services billed, subject to applicable documentation, coding, and payer requirements.

A denial is not just a negative payment outcome. It is evidence about a transaction that needs to be understood.

The system should preserve the relationships among these pieces of information.

Otherwise, the practice receives fragments and must reconstruct the story.

Think of a claim arriving at the end of the process like a stranger carrying a stack of papers and announcing, “I believe you owe me money.”

The billing team has to investigate who the stranger is, what happened, whether the paperwork is complete, and whether the request is valid.

That is not a particularly efficient way to run a revenue cycle.

A more reliable process makes it possible to trace how a claim came to exist, which information supported it, what requirements applied, which actions were completed, and what remains unresolved.

This is the difference between storing data and preserving operational context.

Data answers: “What information do we have?”

Context answers: “What does this information mean for this case?”

Operational memory answers: “What happened, who did what, why did they do it, and what needs to happen next?”

That final question is where the opportunity lies.

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The Denial Is the Crime Scene, Not Necessarily the Crime.

A denial is visible. Its cause may not be.

When a claim is denied, the practice reviews the reason, checks documentation, investigates the payer's decision, and determines whether to correct, appeal, or write off the claim.

That work is often necessary.

But the denial may be the last visible symptom of an earlier failure.

Perhaps eligibility was checked too early and not reverified after a change. Perhaps a referral was received but never connected to the appointment. Perhaps an authorization was obtained but not associated with the service. Perhaps the documentation was complete but the claim contained a mismatch.

Or perhaps the payer made an error despite the practice having followed the applicable requirements.

These causes are different. They require different responses.

A useful system should not treat every denial as proof that the practice made a mistake. Nor should it assume that every denial is the payer's fault.

The better question is:

At what point did the conditions for a successful claim stop being true—and what evidence supports that conclusion?

That changes the work.

Instead of repeatedly correcting individual claims, the practice can identify patterns, investigate root causes, and determine which failures can be prevented.

A recurring eligibility problem might call for a change in verification timing.

An authorization problem might require better tracking of scope, expiration, or service matching.

A payer-specific pattern might require a contract review or a documented escalation strategy.

A documentation issue might require clinician education or a better information-capture process.

A system defect might require a technical change.

The correct response depends on the evidence.

The faster the practice can identify a recurring failure, the less time it spends rediscovering the same lesson.

The objective is not to eliminate every exception. It is to stop treating every exception as if it were the first one ever encountered.

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The Human Cost of Reconstructing Information

Fragmented information affects more than the billing department.

A physician may be asked to clarify documentation long after the encounter.

A front-desk employee may repeat an eligibility check because the previous result cannot be found or trusted.

A billing specialist may spend time locating information another team member already collected.

A patient may receive an unexpected bill while the practice investigates an unresolved payer issue.

A practice owner may see revenue delayed without knowing whether the cause is payer behavior, an internal process failure, or both.

The American Medical Association's prior authorization survey, reported in May 2026, illustrates the broader administrative burden.

The survey found that physicians reported an average of 40 prior authorization requests per week, consuming an average of 13 hours of physician and staff time. Ninety-five percent said prior authorization delayed access to necessary care, while 94% said it contributed to physician burnout.

These findings concern prior authorization specifically. They should not be misrepresented as measurements of all billing inefficiency.

But they raise a relevant question for independent practices: how much administrative effort is unavoidable, how much is driven by external requirements, and how much is caused by the way information and responsibility are organized internally?

A practice cannot eliminate every payer requirement.

It can investigate whether its own systems make compliance harder than necessary.

That distinction matters. It prevents practices from blaming themselves for problems they cannot control—and from overlooking problems they can fix.

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Healthcare Is Buying More AI. Is It Getting More Value?

On October 23, 2026, MobiHealthNews reported on a HIMSS AI in Healthcare Forum discussion about measuring AI by adoption, outcomes, operational performance, and cost.

Nassib Chamoun, founder and CEO of the Health Data Analytics Institute, posed the question featured at the beginning of this article: is there an economic or operational impact that translates to dollars?

The point is important because technical performance is only one part of the equation.

A system can perform well in a test environment and still struggle in daily operations. Integration takes time. Staff need training. Data may be incomplete. A tool may require ongoing maintenance. A workflow may be technically automated but still require substantial human intervention.

The demo is not the business outcome.

For an independent clinic, the right questions include:

• Has the number of manual touches per claim declined?
• Is less time spent searching for information?
• Are avoidable corrections decreasing?
• Are recurring errors being prevented or merely processed faster?
• Can staff identify the next action without opening several systems?
• Does the benefit justify implementation, maintenance, and oversight costs?

A practice could adopt five new tools and still require staff to copy information between them, reconcile conflicting records, check multiple portals, and determine which system contains the authoritative answer.

The technology inventory would look impressive.

The employee's day might look exactly the same.

That is why a better question is not, “How much of our revenue cycle have we automated?”

It is:

“How much unnecessary work has disappeared?”

That is the measure that matters.

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Three Perspectives That Challenge the Status Quo

Atul Gawande: Expertise Needs Reliable Systems

In The Checklist Manifesto and Better, surgeon and writer Atul Gawande explored how structured practices can help professionals manage complexity and improve reliability.

The lesson is not that healthcare workers should be replaced by checklists or software.

It is that expertise alone is an unreliable substitute for systems that make critical information and steps consistently available.

A highly experienced biller may know how to handle a difficult claim. But the organization should not need that person's memory to rediscover the same requirements every time a similar case appears.

Practical implication: Make important steps visible, make exceptions explicit, and preserve the information needed to repeat successful work.

W. Edwards Deming: Fix the System, Not Just the Last Person in the Chain

Deming's work on quality management emphasized improving the system in which people work.

Applied to medical billing, that means examining recurring failures as patterns rather than treating every one as an isolated staff error.

If authorization information repeatedly fails to reach the billing team, another reminder email may not be the answer.

Investigate the handoff.

Who owns the information? Where is it recorded? How does the next person know it is complete? What happens when the information is missing or inconsistent?

Practical implication: Measure recurring failures, identify their causes, and redesign the conditions that produce them.

Nassib Chamoun: AI Needs to Prove Its Value

Chamoun's October 23 comments at the HIMSS AI in Healthcare Forum emphasized the need to look beyond model performance to operational and economic impact.

For an independent practice, that is a useful standard. A tool is not valuable simply because it uses AI, offers an impressive interface, or completes a task quickly.

It is valuable when it solves a real problem at a cost and risk the practice can justify.

Practical implication: Evaluate technology against measurable outcomes, not feature lists or marketing claims.

Together, these perspectives point toward a common principle:

Reliable operations require capable people, useful technology, and a system that preserves information, clarifies responsibility, and makes the next appropriate action visible.

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A Record Is Not the Same Thing as a Memory

Let us return to Amritpal's ICU diary.

The nurses did not simply collect disconnected observations. They preserved a sequence of events in a form that helped him understand his experience later.

The diary connected details to a timeline and a human context.

Medical billing needs a different kind of operational record, but the principle is relevant.

A folder containing an authorization, a clinical note, a claim, a remittance advice, and an appeal letter may contain all the necessary information. Yet a staff member may still have to determine how those documents relate to one another.

A useful operational system should help answer:

• What happened?
• When did it happen?
• Who was responsible?
• What information supported the action?
• Which requirement or policy applied?
• What remains unresolved?
• What is the next action, and by when?
• If the decision is challenged, can the practice reconstruct the reasoning?

That last question becomes especially important as AI enters revenue-cycle operations.

It is not enough for a system to produce an answer. The practice needs to understand the basis for consequential actions, verify the supporting information, correct mistakes, and determine who is accountable.

A confident answer without a traceable basis can create a new problem while appearing to solve an old one.

Healthcare does not need automation that merely sounds certain.

It needs automation that can show its work.

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The Next Generation of AI Should Not Just Answer Questions. It Should Help Finish Work.

Much of the public conversation about AI focuses on chat interfaces, generated text, summaries, and faster responses.

Those capabilities can be valuable.

But a medical practice does not collect revenue because someone wrote a clever paragraph about a denial.

Revenue-cycle work requires a sequence of connected actions: identify the problem, retrieve relevant information, check applicable requirements, determine the next appropriate step, route the work to the right person, and verify that the outcome occurred.

A system that identifies an authorization mismatch but leaves the staff member to locate the authorization, determine ownership, set a reminder, and follow up has solved only part of the problem.

A more useful system would connect detection to action while preserving appropriate human oversight.

For example, imagine a claim-preparation process that identifies missing information before submission.

Instead of merely displaying an error, the system could identify the missing item, locate relevant existing records where authorized, explain what remains unverified, assign the issue to the appropriate role, and track its resolution.

If the information cannot be confirmed, the system should say so.

If the situation requires clinical judgment, payer interpretation, or human approval, the system should escalate rather than improvise.

If an automated action occurs, the practice should be able to inspect what happened and why.

This is not a promise that every claim can be made error-free or every denial eliminated. Payers make decisions, rules vary, clinical circumstances differ, and some disputes are unavoidable.

It is a different definition of progress.

The objective is not to automate the appearance of work. It is to reduce the amount of work required to reach a reliable outcome.

That is where AI can move beyond being another interface and become a meaningful part of operational infrastructure.

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A Practical Plan for Independent Practices

A practice does not need to rebuild its entire technology stack to start improving information flow.

It needs to identify where information is lost, where work is repeated, and where responsibility becomes unclear.

Step 1: Choose One Painful Process

Do not begin by attempting to automate the entire revenue cycle.

Choose one recurring problem, such as eligibility verification, authorization tracking, missing documentation, claim-status follow-up, or denial resolution.

Select a process with enough volume to measure but a manageable scope.

A narrow pilot makes it easier to identify whether a change actually improves performance.

Step 2: Map What Really Happens

Document what staff actually do—not merely what the procedure manual says they do.

Where is information captured? Which systems are consulted? When is information copied manually? Who owns each step? What happens when a required item is missing?

Where do staff rely on memory, informal messages, or personal relationships to move work forward?

The purpose is not to criticize staff. It is to reveal the hidden work required to keep the process functioning.

Step 3: Establish a Baseline

Measure the process before introducing a new tool.

Useful measures include:

• Manual touches per claim.
• Time spent locating or reconciling information.
• Percentage of claims requiring avoidable correction.
• Denial rate by category and payer.
• Days in accounts receivable, interpreted alongside payer mix and contract terms.
• Time from identifying an exception to resolving it.
• Percentage of tasks with clear ownership and a documented next action.

Do not assume every denial is preventable. Separate payer-driven, coverage-related, documentation-related, coding-related, eligibility-related, and other causes where the available evidence supports those categories.

A useful baseline distinguishes what the practice can change from what it cannot.

Step 4: Fix the Information Handoff

Before buying more software, ask whether existing systems can preserve the necessary information and make it available to the next person.

Can authorization details be linked to the relevant appointment or service?

Can claim status and follow-up responsibility be made visible?

Can staff see which information is confirmed, missing, outdated, or conflicting?

Can the practice establish a reliable source of truth for each critical field?

The answer may involve configuration, training, integration, process redesign, or new software. The right intervention depends on the cause.

Step 5: Define Ownership and Escalation

Every unresolved issue should have a clear owner, a next action, and an appropriate deadline.

A status label that says “pending” is not a plan.

Pending with whom? Pending for what? Since when? What happens if nothing changes?

A well-designed process makes those questions answerable without requiring a staff member to remember the history.

Step 6: Automate the Repetitive Work That Remains

Once the process is understood, automate appropriate repetitive tasks.

Examples may include routine status checks, reminders, routing, data validation, and identification of missing information.

Preserve human review for ambiguous cases, clinical decisions, exceptions requiring judgment, and actions with material consequences.

Do not allow automation to silently invent missing data, alter clinical documentation, or represent an unverified requirement as satisfied.

Step 7: Review Results and Adjust

Compare performance against the baseline.

Did manual touches decline? Did staff spend less time locating information? Did the targeted error category improve? Did unresolved exceptions decrease? Did the system create new work elsewhere?

A pilot that fails to improve the intended outcome still provides useful information—provided the practice examines the result honestly.

The objective is not to prove that the software was a good purchase.

The objective is to discover what actually works.

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The Metrics That Matter More Than an AI Demo

A polished demonstration can make almost any workflow look simple.

Real practice operations are less cooperative.

Before purchasing an AI or revenue-cycle product, ask how it will affect measurable outcomes.

Operational reliability

Measure the percentage of tasks completed correctly the first time, the number of manual touches per claim, and the frequency of missing or conflicting information.

Resolution speed

Track the time from identifying an exception to assigning it, acting on it, and resolving it. Distinguish delays caused by the practice from delays caused by payers or other external requirements.

Financial performance

Monitor denial rates by category, avoidable rework, days in accounts receivable, collection performance, and write-offs. Interpret each measure in context; payer mix, specialty, contract terms, and case complexity matter.

Staff capacity

Measure time spent on repetitive follow-up, information retrieval, duplicate entry, and manual reconciliation. The purpose is to determine whether staff gain capacity for higher-value work—not merely whether a task has moved to another screen.

Auditability and safety

Measure whether the system can show the information used, actions taken, changes made, and approvals obtained. Review how it handles uncertainty, incorrect inputs, access controls, and exceptions.

One particularly useful measure is touches per claim.

If the number of touches falls while accuracy and appropriate oversight remain strong, the practice may be removing unnecessary work.

If the software reduces clicks but creates more corrections, escalations, or downstream denials, the improvement may be cosmetic.

The most important question is not how quickly a system can process a claim.

It is how reliably the practice can move from the patient encounter to an appropriate financial resolution.

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Seven Myths That Deserve Retirement

Myth 1: “We need more automation.”

Sometimes. But if the process is poorly defined, automation can replicate the same problems at greater speed.

Better question: What recurring failure are we trying to prevent?

Myth 2: “Our EHR already contains the information.”

Perhaps. But the information may not be structured, current, connected to the relevant transaction, or available to the person who needs it.

Better question: Can the next person find and use the right information without reconstructing the case?

Myth 3: “Our experienced biller knows how to handle it.”

That expertise is valuable. It is also vulnerable if it exists only in one person's memory.

Better question: Can the organization preserve the knowledge and process so the team can use them consistently?

Myth 4: “AI will eliminate denials.”

No responsible vendor should promise that. Some denials reflect payer decisions, coverage limits, legitimate documentation requirements, or disputes that require human judgment.

Better question: Which categories of avoidable errors can we reduce, and how will we prove it?

Myth 5: “A higher automation percentage means a better operation.”

Not necessarily. A highly automated process can still produce errors or require extensive human intervention.

Better question: What work disappeared, what errors declined, and what new risks emerged?

Myth 6: “The cheapest tool is the most affordable.”

A low subscription price may conceal integration expenses, training, support, duplicate workflows, or additional manual work.

Better question: What is the total cost of operating the process before and after implementation?

Myth 7: “The problem is always the payer.”

Payer complexity is real. So are internal process failures. Treating every problem as the payer's fault prevents a practice from fixing failures it can control.

Better question: What evidence identifies the cause, and which part of the problem is within our control?

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What to Look for When Evaluating a Revenue-Cycle Tool

An independent practice should not need a technology department the size of a hospital's to evaluate whether a product is useful.

Start with practical questions.

Does the product fit the existing environment?

Ask which EHRs, practice management systems, clearinghouses, and data formats it supports. Clarify what requires integration, what remains manual, and what happens when an interface fails.

Does it preserve context?

Determine whether the system connects information to the relevant patient, encounter, authorization, claim, payer, and task. Ask how it handles conflicting or outdated data.

Can it explain its actions?

Ask whether staff can inspect the source information, decision rules, action history, and approvals associated with an automated outcome.

Does it handle uncertainty responsibly?

A system should distinguish verified information from inference. It should escalate ambiguous or consequential situations rather than manufacture certainty.

Does it reduce work across the process?

Request a pilot with baseline measurements. Count manual touches, time spent, corrections, and unresolved exceptions—not just the number of automated tasks.

Can the practice control access and protect information?

Review role-based permissions, authentication, encryption, logging, retention, vendor access, and incident response. Confirm whether a business associate agreement is required and in place before protected health information is handled by a vendor acting as a business associate.

What happens when the product is wrong?

Ask how errors are detected, corrected, reported, and reviewed. Establish who approves consequential actions and who remains accountable for operational decisions.

The strongest vendor conversations are not demonstrations of what AI can say.

They are discussions about what the system can reliably do, what it cannot do, and how the practice will know the difference.

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Efficiency Is Not Permission to Ignore Responsibility

Healthcare automation operates within legal, contractual, clinical, and ethical constraints.

HIPAA obligations may apply to the practice and to vendors handling protected health information on its behalf. Depending on the relationship, a business associate agreement and appropriate safeguards may be required.

Access should be limited to authorized users and legitimate purposes. Practices should evaluate how information is transmitted, stored, retained, and deleted, and how security incidents are handled.

Billing and coding must remain consistent with applicable documentation, coverage, and coding requirements. AI should not be used to manufacture documentation, upcode services, create unsupported diagnoses, or submit claims based on facts that have not been established.

Automation should also respect the distinction between administrative support and clinical judgment. A tool that assists with workflow should not quietly assume authority over decisions it is not qualified or authorized to make.

Finally, accountability must remain clear.

If an AI system identifies an issue, someone should know whether it is a recommendation, a draft action, or an executed action. Staff should be able to inspect consequential decisions and correct errors.

The more autonomous a system becomes, the more important it is to define its boundaries, oversight, and audit trail.

Automate work. Do not automate away responsibility.

Practices should obtain appropriate legal, compliance, and technical advice for their specific circumstances rather than treating a software vendor's assurances as a substitute for due diligence.

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Where Healthcare AI Is Headed

The next phase of healthcare AI will not be defined solely by which model produces the most impressive response.

It will increasingly be defined by whether systems can operate reliably inside real organizations.

That means connecting information across systems, maintaining state, respecting permissions, handling exceptions, and providing evidence of what occurred.

In revenue-cycle operations, this could mean identifying a missing authorization before claim submission, surfacing an unresolved eligibility issue, routing a task to the right person, monitoring a deadline, and preserving the history needed to understand the result.

It could also mean recognizing that a problem cannot be solved automatically and escalating it before a deadline is missed.

The opportunity is substantial, but it should not be confused with a promise of perfect automation.

Payer requirements will continue to change. Clinical documentation will remain nuanced. Contracts will differ. Some claims will require appeals, negotiation, or human review. Systems will occasionally fail.

The goal is to make those realities manageable without forcing staff to reconstruct the same information repeatedly.

The future may contain fewer disconnected portals, fewer manual handoffs, and fewer situations in which the only person who understands a process is the employee who has worked there for twenty years.

That would be progress.

Not because technology has replaced human expertise, but because it has made that expertise easier to use, preserve, and share.

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What This Means for OnnX

At OnnX, we approach medical billing from a different starting point.

Instead of assuming the answer is simply more downstream automation, we focus on the relationship between data, workflows, and revenue.

The premise is that many operational problems become harder and more expensive when information is incomplete, disconnected, or unavailable at the moment a decision must be made.

A denial may be the first visible sign of a problem that began much earlier.

A claim may require repeated correction because a prior step did not preserve the information the next step needed.

A billing team may spend hours chasing status updates because ownership, dependencies, and deadlines are not visible in one coherent operational process.

The opportunity is to address those problems as connected systems—not as a growing collection of isolated tasks.

For independent practices, the distinction matters.

A large health system may have specialized teams and substantial IT resources to absorb operational complexity. A small practice has fewer people, tighter margins, and less capacity to absorb repeated administrative work.

When a process depends on a particular employee's memory, the practice carries an invisible operational risk.

When a system preserves the relevant information, tracks the work, and makes the next appropriate action visible, the organization can become less dependent on informal workarounds.

That is the direction OnnX is working toward: using AI-enabled operational intelligence to help independent clinics make their revenue-cycle processes more consistent, traceable, and manageable.

This is a product philosophy, not a claim that every capability is already deployed or that all billing problems can be eliminated. Specific results must be established through implementation, measurement, and validation.

Our underlying belief is that independent physicians should not have to choose between caring for patients and building a financially sustainable practice.

Predictable revenue is not the purpose of medicine.

But without financial stability, independent practices have less freedom to invest in their teams, serve their communities, and remain independent.

The work of improving medical billing should therefore begin before the denial, before the appeal, and often before the claim itself.

It should begin with the information that makes the entire process possible.

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The Question We Should Be Asking

Amritpal Virdee's ICU diary mattered because it preserved details that could otherwise have disappeared.

It helped him reconnect with a part of his life that he could not remember.

No one should confuse that deeply personal experience with a delayed insurance payment. The human consequences are different, and the comparison has limits.

But the story reminds us of a broader principle: information has value beyond the moment it is first recorded.

Its value depends on whether it remains available, understandable, and connected to the decisions that follow.

Healthcare has invested enormous effort in digitizing records and automating tasks.

The next challenge is to make the information within those systems work together.

For medical practices, that means fewer mysteries about why a claim failed, fewer hours spent searching for information already collected, and clearer accountability for what happens next.

For AI developers, it means moving beyond systems that merely generate answers toward systems that support reliable, traceable work.

For practice owners, it means evaluating technology by the burden it removes—not the number of features it adds.

And for the healthcare industry, it means recognizing that a sophisticated system can still be operationally forgetful.

We do not need every medical practice to become a technology company.

We need technology to make running a medical practice less unnecessarily complicated.

The next breakthrough in medical billing may not be another tool that processes claims faster. It may be a system that makes fewer claims require rescue in the first place.

That is a more interesting goal than simply adding AI to the billing department.

And it is a much better reason to build technology.

One final question for physicians and practice owners: If your billing team disappeared for two weeks, would your systems preserve the work—or would your practice discover that its most important operating manual was stored in someone's head?

I would love to hear how physicians, practice administrators, and revenue-cycle professionals are tackling this problem.

What consumes more time in your practice today: doing the work, or figuring out what happened to work someone already did?

Share your experience in the comments. If this perspective would help another physician or practice owner, consider reposting the article.

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About the Author

Dr. Daniel Cham is a physician, medical consultant, and healthcare technology entrepreneur. He is the founder of OnnX, which focuses on AI-enabled approaches to medical billing and revenue-cycle operations for independent medical practices.

His work explores how better information architecture, operational workflows, and responsible automation can help physicians spend less time navigating administrative complexity and more time building sustainable practices.

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For a free OnnX resource, visit the Featured section of my LinkedIn profile. The resource is intended to provide practical insight without requiring a signup.

Knowledge creates opportunity for progress.

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References and Further Reading

1. Sandwell and West Birmingham NHS Trust. “ICU diary helps heart attack patient piece together missing memories.” October 23, 2026. The original story of Amritpal Virdee, his family, and the ICU diary.
2. MobiHealthNews. “Healthcare AI’s value depends on adoption, outcomes and costs.” October 23, 2026. Reporting on the HIMSS AI in Healthcare Forum, including Nassib Chamoun’s comments about measuring operational and economic impact.
3. American Medical Association. “AMA survey: Prior authorization reform pledge falls short with physicians.” May 2026. Survey findings on physician workload, delayed care, and prior authorization.

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Disclaimer

This article is intended for general educational and informational purposes. It does not constitute medical, legal, coding, reimbursement, compliance, or financial advice. Healthcare billing requirements vary by payer, contract, specialty, jurisdiction, and service. Practices should consult qualified professionals regarding their specific circumstances. The discussion of OnnX describes its guiding approach and intended value; it is not a guarantee of financial, clinical, or operational results.

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