We taught artificial intelligence to predict insurance denials, automate appeals, and chase payments. But what if the biggest breakthrough in medical billing is making those problems unnecessary?
“The secret of the care of the patient is in caring for the patient.” — Dr. Francis W. Peabody, 1927
The Patient Got Better. The Insurance Claim Didn’t.
Medicine treats diseases. Healthcare administration sometimes manufactures them.
Imagine this.
It’s Friday, 6:45 p.m.
A family physician has finished seeing patients. The waiting room is empty. The staff has gone home.
But the doctor is still sitting in front of a computer.
Not because a patient is critically ill.
Not because an emergency requires attention.
Because an insurance company has denied payment for care already delivered.
The reason?
A missing authorization.
An eligibility mismatch.
A diagnosis code that doesn’t match the documentation.
The patient received appropriate treatment. The physician did the work. The medical record exists.
And now everyone gets homework.
The billing specialist investigates. The front desk makes another phone call. The physician clarifies the documentation. Someone prepares an appeal.
The clinic waits for payment.
The insurer waits for more information.
The patient wonders why healthcare is so complicated.
And somewhere, another software company announces an exciting new artificial intelligence solution to automate the entire argument.
Congratulations. We’ve invented faster bureaucracy.
This is an illustrative scenario, not a documented patient or OnnX customer case. But its underlying frustrations are familiar to many independent practices.
As a physician and healthcare entrepreneur, I’ve become increasingly interested in a question that rarely receives enough attention:
Why are we spending so much money teaching technology to repair administrative mistakes instead of designing systems that prevent them?
We have artificial intelligence that can analyze medical images, interpret clinical language, and process enormous amounts of information.
Yet a missing insurance field can still delay a physician’s payment.
We have robots assisting with surgery.
But apparently, verifying a patient’s insurance before an appointment remains an extraordinary technological achievement.
Something doesn’t add up.
And I don’t think the answer is another dashboard.
Ten Uncomfortable Truths About Medical Billing
Before we explore the details, here are ten ideas worth challenging your next practice management meeting with.
- A busy clinic isn’t necessarily a profitable clinic. Sometimes it’s just a more efficient producer of paperwork.
- A paid claim isn’t necessarily a correctly paid claim. Payment and payment accuracy are different things.
- The best denial is the one that never happens. Prevention can be more valuable than recovery.
- Artificial intelligence can automate mistakes as efficiently as it automates solutions. Bad information doesn’t become good information because an algorithm processes it.
- More billing staff won’t automatically fix a broken workflow. Sometimes it simply increases the number of people managing the same problem.
- A high appeal success rate can hide a poor upstream process. Winning arguments isn’t the same as avoiding unnecessary arguments.
- Physician burnout isn’t always a resilience problem. Sometimes the job has been designed beyond reasonable human capacity.
- More technology doesn’t automatically mean less work. Every new system can become another system someone must manage.
- Revenue cycle management begins before the patient sees the physician. By the time a claim is denied, the original mistake may be days or weeks old.
- The future of medical billing may be less about billing and more about information quality. Get the information right early, and many downstream problems become easier to prevent.
The uncomfortable part?
Most of these ideas aren’t revolutionary.
They’re obvious.
And sometimes the most expensive problems are the ones we’ve become too accustomed to question.
Healthcare’s Impossible Workday: When 24 Hours Isn’t Enough
You cannot solve a mathematical impossibility with a motivational poster.
A frequently cited primary care workload model estimated that completing recommended care and administrative responsibilities for a typical patient panel could require approximately 26.7 hours per day.
Twenty-six point seven hours.
On a planet that stubbornly provides only twenty-four.
The estimate is a modeling result, not a measurement of a physician’s actual daily working hours. But it illustrates the scale of competing demands.
And what has been one of healthcare’s traditional responses?
Work harder.
See more patients.
Complete documentation faster.
Answer more messages.
Attend another productivity meeting.
Maybe download a mindfulness app.
Apparently, the solution to an impossible workload is to become a more relaxed person while doing it.
As physicians, we’re trained to identify the underlying cause of a problem.
If a patient repeatedly develops the same complication, we investigate why.
We don’t simply congratulate ourselves for treating the complication faster each time.
Yet in healthcare operations, we often do exactly that.
We treat the symptoms of inefficient systems while leaving the underlying causes untouched.
The volume trap
Consider an independent clinic facing declining margins.
Reimbursement is under pressure. Staffing is expensive. Technology subscriptions keep multiplying.
The instinctive response is to see more patients.
More visits mean more revenue.
At least in theory.
But more visits also mean more documentation, claims, authorizations, laboratory results, patient messages, and administrative follow-up.
If the underlying workflows are inefficient, increasing volume can magnify the inefficiency.
When your system leaks money, increasing volume may simply increase the size of the leak.
That doesn’t mean patient volume is unimportant.
It means volume must be supported by reliable operations.
A practice should understand its collection performance, administrative cost per encounter, and capacity before assuming that a fuller schedule will solve its financial problems.
The goal isn’t to see fewer patients. It’s to stop wasting the resources needed to care for them.
The Numbers Behind the Madness
Healthcare administration has become so complicated that even describing the problem can feel like another administrative task.
But several figures deserve attention.
The American Medical Association’s 2025 prior authorization survey, released in 2026, reported the following physician experiences.
40 prior authorization requests per physician each week.
13 hours of combined physician and staff time spent on prior authorization weekly.
95% of surveyed physicians reported delays in access to necessary care.
94% reported that prior authorization contributes to physician burnout.
26% reported a serious adverse event associated with prior authorization for a patient in their care.
These are physician-reported survey findings, not independently verified incidence rates for all patients or practices.
Still, the broader picture is troubling.
We have created a healthcare environment where administrative requirements can consume substantial clinical resources.
And then we wonder why physicians feel exhausted.
The healthcare system doesn’t merely ask doctors to care for patients. It asks them to repeatedly prove that caring for patients was administratively permissible.
Prior authorization can serve legitimate purposes, including utilization review and coverage administration.
But a legitimate objective doesn’t excuse an inefficient implementation.
The real question is whether the process achieves its intended goals without creating disproportionate burdens or delaying appropriate care.
The financial consequences are easy to underestimate
Imagine a clinic repeatedly correcting the same eligibility problem.
Each correction requires staff time.
Some claims are delayed.
Others require resubmission.
A few may become difficult or impossible to recover.
Now multiply that across hundreds of encounters.
The cost isn’t only the value of denied claims.
It’s also the labor spent identifying, correcting, and monitoring errors.
A claim that eventually gets paid can still be expensive to collect.
That’s why physicians should monitor not only revenue, but also the effort required to generate that revenue.
A practice can appear financially successful while quietly spending too much time managing preventable problems.
Three Medical Experts, Three Lessons Healthcare Keeps Ignoring
The debate over artificial intelligence in healthcare often sounds like a contest between technology enthusiasts and technology skeptics.
I think that’s the wrong argument.
The better question is how technology, professional judgment, and system design should work together.
Three established medical voices offer useful perspectives.
Expert One: Dr. Bruce A. Scott — Administrative Reform Should Be Measured by Its Effect on Patients
Dr. Bruce A. Scott, a former president of the American Medical Association, has been associated with the organization’s advocacy for meaningful prior authorization reform.
The AMA has repeatedly highlighted how authorization requirements affect physician workload and patient access.
Here’s the question I take from that work.
If an administrative process becomes faster but patients still wait unnecessarily for care, how much have we actually improved?
We often measure the number of authorizations processed.
But we should also measure how many appointments were delayed, how many procedures were rescheduled, and how much staff time was consumed.
Speed is useful.
Appropriate clinical outcomes and timely access matter more.
Practical advice: Track authorization-related patient delays alongside operational completion rates.
Don’t celebrate processing efficiency while ignoring the patient’s experience.
Expert Two: Dr. John Halamka — Artificial Intelligence Is Only as Trustworthy as the Information Behind It
Dr. John Halamka, a physician and healthcare technology leader associated with Mayo Clinic Platform, has emphasized the importance of reliable data, interoperability, and responsible technology governance.
That perspective is especially relevant to medical billing.
Artificial intelligence can review thousands of claims.
It can identify missing information.
It can suggest codes.
It can detect patterns.
But what happens when the original documentation is incomplete?
Or the insurance information is outdated?
Or a payer rule is incorrectly configured?
Artificial intelligence doesn’t magically repair bad information. Sometimes it gives bad information a much faster delivery system.
The familiar technology principle is garbage in, garbage out.
Healthcare has added a twist.
Garbage in, expensive appeal out.
Practical advice: Before adopting an AI billing platform, ask where its information comes from, how accuracy is verified, and how errors are corrected.
A vendor’s impressive demonstration is not the same as validated performance in your practice.
Expert Three: Dr. Atul Gawande — Stop Depending on Heroes to Rescue Broken Systems
Dr. Atul Gawande, surgeon, author, and healthcare improvement advocate, has helped bring attention to checklists, teamwork, and system design.
His work challenges the idea that expertise alone can overcome every operational failure.
Medicine is too complicated for success to depend on one person remembering everything.
Medical billing is no different.
If your clinic depends on one extraordinary billing specialist who knows every payer’s quirks, you may have a talented employee.
You may also have a fragile business.
What happens when that person takes a vacation?
Or retires?
Or decides to pursue a career that doesn’t involve arguing with insurance companies?
A reliable system shouldn’t require a hero every Friday afternoon.
It should make routine work consistent and reserve human expertise for situations that genuinely need it.
Practical advice: Document recurring workflows, establish ownership, and create escalation rules for exceptions.
These expert perspectives are interpretations of their established public work, not quotations from new interviews conducted for this article.
The shared lesson
Dr. Scott’s advocacy emphasizes patient access.
Dr. Halamka’s work highlights reliable information and governance.
Dr. Gawande’s systems thinking emphasizes process reliability.
Together, they point toward one conclusion.
Healthcare doesn’t need to choose between technology and people. It needs to stop putting capable people inside incapable systems.
Medical Billing’s Biggest Secret: The Problem Often Begins Before Billing
You can’t fix a leaking roof by buying a better mop.
Yet that’s how much of medical billing operates.
The clinic delivers care.
A claim is submitted.
The insurer rejects or denies it.
The billing department investigates.
Someone corrects the problem.
And the cycle repeats.
The industry has become remarkably sophisticated at managing the consequences.
But the original mistake may have occurred before the patient entered the examination room.
An outdated insurance record.
A missing referral.
An authorization requirement nobody identified.
A documentation gap.
A service that wasn’t captured correctly.
These are not always coding problems.
They’re often information and workflow problems.
Five checkpoints that deserve more attention
1. Patient registration
Confirm demographics and insurance identifiers.
A small registration error can create downstream complications.
2. Eligibility verification
Check coverage and applicable benefits before the visit whenever feasible.
For scheduled encounters, a previsit verification window can give staff time to resolve discrepancies.
3. Prior authorization
Identify authorization requirements, deadlines, and ownership.
A tracker is useful only if someone actually monitors it.
4. Clinical documentation
Ensure records accurately reflect the patient’s condition, medical decision-making, and services performed.
Documentation must serve clinical truth, not merely reimbursement.
5. Charge capture
Confirm that appropriate services are recorded and transferred correctly.
The most sophisticated billing software cannot reliably collect revenue for services that were never captured.
These checkpoints aren’t glamorous.
Nobody is likely to announce a revolutionary conference keynote titled, “We Finally Verified Insurance Correctly.”
But perhaps they should.
Sometimes innovation is simply making the obvious consistently reliable.
We Automated the Denial. Then We Automated the Appeal. Now What?
Here’s a possible future for American healthcare.
An insurance company deploys artificial intelligence to identify claims requiring additional review.
A physician practice deploys artificial intelligence to predict those denials.
Another system prepares appeals.
The insurer’s system reviews the appeals.
The provider’s system challenges the decisions.
And both sides celebrate their technological progress.
Meanwhile, the patient wonders why the bill is still unresolved.
Welcome to the artificial intelligence arms race nobody asked patients to finance.
This scenario is illustrative, not a claim that every insurer or provider uses these tools in this way.
Artificial intelligence can genuinely improve medical billing.
It may help identify missing information, review documentation, suggest coding improvements, reconcile payments, and prioritize follow-up.
Those are meaningful applications.
But the value depends on what the system actually changes.
Two very different types of innovation
The first approach asks:
How can we predict which claims will be denied?
The second asks:
Why are these claims being denied, and what can we change before submission?
The first can improve recovery and prioritization.
The second can reduce avoidable friction.
Both matter.
But a prediction engine that never changes the underlying workflow may simply become a more sophisticated warning system for problems we continue creating.
Imagine a fire alarm that becomes progressively better at predicting fires.
Wonderful.
Now imagine someone asks why the building keeps catching fire.
That’s the conversation I want healthcare to have.
The role of human judgment
Not every denial is preventable.
Not every payer disagreement can be solved by better documentation.
And not every coding decision should be automated.
Complex clinical cases require qualified professionals.
Payer rules can be ambiguous.
Coverage decisions may involve legitimate disputes.
The goal is not to eliminate billing specialists.
It’s to stop wasting their expertise on problems that a better process could have prevented.
Let machines handle repetition. Let people handle judgment. Let better design eliminate unnecessary work.
Recent News: Healthcare AI Is Getting Smarter. Is Healthcare Getting Simpler?
Recent developments during the week of November 2–8, 2026, illustrate both the opportunities and limitations of revenue-cycle automation.
AI coding technology reports major cost reductions
Healthcare IT News reported on November 3 that the Bone and Joint Institute of Tennessee achieved an 80% reduction in surgical coding costs through technology integrated with its electronic health record.
That’s a striking reported result.
It demonstrates the potential value of targeting a specific administrative expense.
But it doesn’t mean every orthopedic practice, family clinic, or specialty group can reproduce the same outcome.
Results depend on workflow, case complexity, implementation, and measurement.
The lesson isn’t that AI guarantees an 80% cost reduction. It’s that technology should be judged by measurable operational improvement.
Hidden revenue gaps are attracting attention
On November 5, Medical Economics highlighted an educational discussion involving AI scribes, revenue gaps, and financial conversations with patients.
That combination is revealing.
Documentation technology, revenue integrity, and patient communication are often treated as separate issues.
In reality, they influence one another.
Incomplete documentation can affect billing.
Poor financial communication can create patient frustration.
Administrative inefficiency can consume resources that would otherwise support care.
Healthcare doesn’t have isolated administrative problems. It has connected workflows that often fail to communicate.
A paid claim may still conceal lost revenue
A November 7 MarketScale article examined underpayments in healthcare revenue cycles.
The central point deserves attention.
A claim can be accepted.
A claim can be paid.
And the reimbursement can still be lower than the applicable contract allows.
Getting paid is an event. Getting paid correctly is a financial control.
For independent practices, that distinction can matter enormously.
A low denial rate is not enough.
Revenue integrity also requires payment verification.
What these developments tell us
Healthcare technology is becoming more capable.
But greater capability doesn’t automatically create a simpler system.
We need to evaluate whether innovation reduces total administrative work, improves accuracy, protects patients, and supports sustainable practice operations.
The real measure of healthcare AI isn’t how much work it can automate. It’s how much unnecessary work disappears.
The Denial Management Paradox: When Success Hides Failure
Imagine a billing department recovers $100,000 in previously denied claims.
That’s an achievement.
The staff worked hard.
The clinic recovered money it was owed.
Management celebrates.
But consider another question.
How much of that $100,000 was tied to preventable problems?
If a substantial portion came from recurring eligibility mistakes, missing authorizations, or documentation gaps, the recovery success tells only part of the story.
The team may have performed exceptionally well inside a flawed process.
Being excellent at fixing mistakes can become an excuse for never eliminating them.
This is not an argument against appeals.
Appropriate appeals protect practices and patients from incorrect payer decisions.
It’s an argument for separating preventable errors from legitimate disputes.
A strong revenue cycle should track both.
How much was recovered?
How much rework was required?
What caused the problem?
Could the problem have been avoided?
Turn denials into operational intelligence
Instead of treating each denial as an isolated event, group denials by their underlying cause.
Look for patterns.
Are certain payers repeatedly involved?
Are errors concentrated in specific appointment types?
Do particular services require additional authorization checks?
Are documentation gaps associated with certain workflows?
Then make changes upstream.
A denial tells you what went wrong. A pattern of denials tells you where to look.
That’s where revenue-cycle data becomes a management tool rather than merely a record of financial frustration.
The 90-Day Plan: Fix the System Without Buying Another Expensive System
Physician-owners don’t need another ambitious transformation program that requires six consultants, four dashboards, and a budget presentation longer than a medical textbook.
Start smaller.
Days 1–30: Find the leak
Review recent claims.
Identify the most common denial reasons.
Measure first-pass acceptance, payment delays, accounts receivable aging, and staff rework.
Ask the front desk, billing staff, and clinicians what repeatedly goes wrong.
Choose one problem.
Not ten.
One.
The goal is clarity, not another committee.
Days 31–60: Fix one upstream process
If eligibility errors are common, establish a reliable verification checkpoint.
If authorizations are frequently missed, assign responsibility and implement tracking.
If documentation is incomplete, develop appropriate clinical documentation standards.
Use technology where it helps.
But don’t confuse purchasing software with improving a process.
A subscription is not a strategy.
Days 61–90: Measure what changed
Compare results with your baseline.
Did preventable denials decrease?
Did staff spend less time on corrections?
Did payment timing improve?
Did the new workflow create other problems?
Ask employees what they learned.
Keep what works.
Change what doesn’t.
Then move to the next problem.
You don’t need to rebuild the entire clinic to stop one expensive mistake from happening again.
The Metrics That Matter — and the Ones That Just Look Impressive
Healthcare loves dashboards.
Colorful charts.
Performance indicators.
Monthly reports.
Sometimes the dashboard becomes so elaborate that someone needs another dashboard to explain it.
But physician-owners need a manageable set of actionable measures.
First-pass acceptance rate
How many claims are accepted for processing on the first submission?
A high rate suggests fewer front-end submission problems, although it does not guarantee payment.
Initial denial rate
How many adjudicated claims are initially denied?
Separate denial reasons to identify recurring causes.
Clean-claim rate
How consistently are claims submitted with the information needed for processing?
Define the metric carefully because industry definitions vary.
Days in accounts receivable
How long does revenue remain outstanding?
Look at payer-level performance and aging categories.
Net collection rate
How much collectible revenue does the practice actually receive after contractual adjustments?
Authorization completion
How consistently are required approvals obtained before the service?
Denial rework time
How many staff hours are spent correcting, resubmitting, and appealing claims?
Payment accuracy
Are claims reimbursed according to applicable payer contracts?
Administrative workload
How much time do employees spend on repetitive tasks that could be simplified or prevented?
Here’s my preferred test.
If a metric doesn’t help someone make a better decision, why are we collecting it?
Measurement should reduce uncertainty.
Not create another reporting obligation.
Seven Expensive Pitfalls Independent Clinics Should Avoid
Pitfall One: Buying AI because everyone else is buying AI
Artificial intelligence isn’t a business strategy.
It’s a capability.
Before adopting it, define the problem, expected improvement, implementation cost, and method of validation.
Pitfall Two: Hiring more people to manage recurring errors
Additional staff may be necessary.
But if the underlying workflow is broken, hiring alone may increase operating costs without reducing error rates.
Pitfall Three: Measuring collections without measuring rework
A claim that is eventually paid may still require substantial administrative effort.
Pitfall Four: Ignoring the front desk
Registration and eligibility workflows influence reimbursement.
The front desk is not merely a scheduling function.
It is an important part of revenue-cycle integrity.
Pitfall Five: Trusting automation without oversight
AI recommendations can be wrong.
Clinical coding, documentation, and compliance decisions need appropriate validation.
Pitfall Six: Confusing a vendor demonstration with proven value
A compelling presentation doesn’t establish effectiveness in your specialty or practice.
Request relevant evidence.
Pitfall Seven: Trying to fix everything at once
Too many simultaneous changes make implementation difficult and outcomes harder to interpret.
The most expensive technology mistake isn’t buying the wrong software. It’s buying software before understanding the problem.
Myth Busters: What Healthcare Keeps Getting Wrong
Myth: More patients always mean more profit.
Reality: More volume can also produce more expenses, administrative work, and errors.
A financially healthy practice needs both appropriate patient volume and operational discipline.
Myth: The billing department is responsible for every billing problem.
Reality: Many reimbursement problems originate in scheduling, registration, authorization, documentation, or charge capture.
Revenue-cycle performance is a team responsibility.
Myth: Artificial intelligence will replace medical billing professionals.
Reality: Automation may reduce routine tasks, but complex cases still require expertise, oversight, and accountability.
Myth: A paid claim means the revenue cycle worked correctly.
Reality: Claims may be underpaid relative to applicable contracts.
Myth: High appeal success means excellent billing performance.
Reality: It may also indicate recurring problems that deserve prevention.
Myth: Physician burnout can be solved through better personal resilience.
Reality: Organizational workload and system design must also change.
Myth: Independent clinics need enterprise budgets to innovate.
Reality: Better processes, clearer accountability, and targeted technology can create meaningful improvements without enormous spending.
The biggest myth of all? That because healthcare has always worked this way, it must continue working this way.
Legal Implications: Automation Doesn’t Automate Responsibility Away
Medical billing involves legal and regulatory obligations.
And the introduction of artificial intelligence doesn’t eliminate them.
HIPAA and patient privacy
Practices must evaluate how vendors access, process, store, and protect protected health information.
Appropriate business associate agreements, access controls, safeguards, and security assessments may be required.
False Claims Act exposure
Unsupported or inaccurate claims can create serious compliance risks.
An AI-generated coding recommendation does not excuse submitting a claim that lacks appropriate support.
Documentation integrity
Medical records should accurately reflect the care provided.
Technology should not invent diagnoses, exaggerate complexity, or alter documentation merely to increase reimbursement.
Payer contracts
Practices must understand filing deadlines, authorization rules, payment provisions, and appeal procedures.
Physician self-referral and ancillary services
Stark Law, the Anti-Kickback Statute, state requirements, and payer rules may apply when practices expand into ancillary services.
Appropriate legal review is important.
Interoperability and prior authorization reform
The Centers for Medicare & Medicaid Services’ interoperability and prior authorization final rule introduces requirements for specified payers, including certain application programming interface obligations beginning in 2027.
These changes may improve electronic information exchange.
But moving information faster doesn’t automatically make the information accurate or the coverage decision appropriate.
The legal lesson is straightforward.
You can automate a task. You cannot automate away accountability.
Ethical Considerations: The Patient Must Not Become a Billing Variable
Revenue-cycle efficiency matters.
Independent practices need to remain financially sustainable.
But financial optimization should never undermine clinical judgment or patient welfare.
Consider an AI system that recommends a billing code.
The code must accurately reflect the documented service.
Consider a system that identifies financially attractive patients.
The practice must still respect applicable laws, ethical obligations, and appropriate access to care.
Consider an automated authorization workflow.
It should not create avoidable treatment delays or conceal clinically important exceptions.
Five ethical safeguards
Protect clinical independence. Physicians should make appropriate care decisions based on patient needs.
Preserve documentation accuracy. Never create clinical facts to support reimbursement.
Maintain human accountability. High-risk decisions require appropriate professional oversight.
Evaluate fairness. Monitor whether automated tools perform differently across patient populations or clinical settings.
Protect privacy. Limit access to sensitive information and maintain appropriate safeguards.
The purpose of administrative innovation should be to support medicine.
Not turn medicine into an administrative optimization exercise.
Patients are not billing codes with vital signs.
The Independent Practice Dilemma: Freedom Comes With an Invoice
Independent physicians face a difficult reality.
They want clinical autonomy.
They want meaningful patient relationships.
They want control over how care is delivered.
But independence also means responsibility for staffing, technology, contracts, compliance, and cash flow.
A physician-owner can finish a full clinical day and still have a second job waiting.
Chief financial officer.
Human resources manager.
Technology troubleshooter.
Occasional printer repair technician.
Medical school prepared us to diagnose disease. It didn’t necessarily prepare us to diagnose why the clearinghouse rejected Tuesday’s claims.
And yet financial sustainability matters.
A clinic that cannot pay its employees cannot continue caring for patients.
The answer isn’t to pretend money doesn’t matter.
It’s to design operations that protect both financial health and clinical priorities.
The question isn’t whether to outsource
Outsourcing can provide valuable expertise.
So can internal billing teams.
So can carefully implemented technology.
The important question is which combination creates the best results for the practice.
A small clinic may benefit from specialized external support.
Another may prefer greater internal control.
A third may use automation for routine checks while retaining experienced staff for complex cases.
There is no universal solution.
The best operating model is the one that improves accuracy, accountability, affordability, and patient care in your actual practice.
Where OnnX Enters the Conversation
As a physician-entrepreneur, I founded OnnX around a question that continues to challenge how I think about healthcare administration.
Why are we building increasingly sophisticated tools to manage problems that better information could prevent?
OnnX is an AI-powered medical billing software initiative focused on reducing administrative friction and unnecessary intermediaries for independent medical practices.
The direction is intentionally different from simply building another denial management platform.
I want to examine what happens earlier.
Before the claim.
Before the rejection.
Before the appeal.
Before the billing specialist spends another afternoon trying to recover money for care already delivered.
That means looking at eligibility, authorization, documentation, charge capture, and the handoffs between clinical and administrative workflows.
It also means recognizing that technology alone won’t solve everything.
Experienced billing professionals understand payer behavior.
Physicians understand clinical context.
Practice managers understand operational constraints.
Compliance specialists understand regulatory risk.
The challenge is to connect that knowledge into more reliable systems.
The philosophy behind OnnX
Shift from denial management to denial prevention.
This is a product-development philosophy and direction, not a claim that all proposed capabilities have been deployed or that particular financial outcomes have already been demonstrated.
I believe the opportunity is to improve the information entering the revenue cycle so fewer avoidable problems emerge downstream.
Not to eliminate every human intermediary regardless of value.
But to reduce unnecessary handoffs, duplicated effort, and preventable administrative work.
The goal isn’t to replace expertise. It’s to stop wasting expertise.
And if we can do that consistently, independent clinics may gain something more valuable than another billing dashboard.
Greater operational control.
More predictable processes.
Less administrative friction.
And more time for the work physicians actually trained to do.
Future Outlook: Will AI Fix Healthcare or Just Make the Bureaucracy Faster?
Artificial intelligence will likely become more capable at interpreting clinical information, identifying billing discrepancies, reviewing payer requirements, and coordinating workflows.
Interoperability may improve information exchange.
Automation may reduce repetitive administrative tasks.
But capability alone isn’t progress.
Imagine a future where insurers and providers each deploy increasingly sophisticated AI systems.
One system flags a claim.
Another disputes the flag.
A third evaluates the dispute.
A fourth generates supporting documentation.
A fifth analyzes the decision.
All within seconds.
Remarkable technology.
But if the original problem was a missing eligibility field, we’ve built a very expensive solution to a very ordinary mistake.
The future of healthcare should not be two artificial intelligence systems arguing at machine speed over paperwork nobody wanted.
It should be a system where accurate information is available when needed, routine tasks are reliable, clinical judgment is protected, and unnecessary disputes are reduced.
For independent practices, the opportunity is to move from reactive financial recovery toward proactive revenue integrity.
That includes prevention.
It also includes appropriate coding, payment verification, contract oversight, and effective appeals when needed.
The objective isn’t zero administration.
It’s administration that actually serves a purpose.
Innovation should be measured by the friction removed, not the complexity added.
Frequently Asked Questions
Can artificial intelligence eliminate insurance denials?
No. Some denials involve coverage disputes, medical necessity determinations, or payer errors.
AI may reduce certain preventable errors, but it cannot guarantee payment.
What is denial prevention?
Denial prevention focuses on identifying and correcting avoidable problems before claims are submitted.
Common areas include eligibility, authorization, documentation, coding accuracy, and charge capture.
How is denial prevention different from denial management?
Denial management addresses denials after they occur.
Denial prevention attempts to reduce avoidable causes before submission.
A reliable revenue cycle requires both.
What should independent physicians improve first?
Start with the practice’s most frequent and costly recurring problems.
Use actual claim and workflow data rather than assumptions.
Does AI replace human medical coders?
AI can assist with coding tasks, but complex cases and compliance-sensitive decisions still require appropriate professional oversight.
Is outsourcing better than AI billing software?
It depends on the practice.
The best approach may combine internal expertise, external specialists, and automation.
How can physicians reduce administrative burnout?
Reduce unnecessary work, clarify responsibilities, improve electronic health record workflows, and address recurring operational failures.
Personal resilience alone is not enough.
What is a clean claim?
A clean claim generally contains the information needed for processing without additional correction.
Definitions can vary among organizations and vendors.
Can a paid claim still represent lost revenue?
Yes. A payer may reimburse less than the applicable contracted amount.
Payment reconciliation and contract verification matter.
What are the most important revenue-cycle metrics?
First-pass acceptance, initial denial rates, denial reasons, accounts receivable aging, net collection rate, authorization completion, and rework time are useful starting points.
Is AI medical billing HIPAA-compliant?
Compliance depends on the specific technology, vendor relationships, safeguards, and information flows.
Practices should conduct appropriate due diligence.
How long does improvement take?
Some operational changes can be implemented within weeks.
Financial effects may require multiple billing cycles to evaluate.
Can smaller clinics benefit from AI?
Potentially, yes.
But adoption should be based on practical value, affordability, security, and measurable outcomes.
Will prior authorization reform eliminate administrative work?
Reforms may reduce certain burdens and improve electronic information exchange.
They won’t necessarily eliminate all authorization requirements or payer disputes.
What is the biggest mistake physician-owners make with technology?
Buying a solution before clearly defining the problem.
A technology purchase should follow operational diagnosis, not replace it.
Final Thoughts: Stop Becoming Better at Problems You Shouldn’t Have
As physicians, we’re trained to treat causes, not merely symptoms.
Yet healthcare administration often rewards us for doing the opposite.
We celebrate successful appeals without asking why the claims were denied.
We celebrate faster documentation without asking why so much documentation is necessary.
We celebrate AI-powered automation without asking whether the underlying task should exist.
And we celebrate physician resilience while continuing to build systems that require extraordinary endurance.
We’ve become so good at managing dysfunction that dysfunction has started to look like an industry.
I don’t believe the solution is to eliminate administration.
Healthcare requires accurate records, responsible payment systems, regulatory compliance, and financial accountability.
But we should distinguish necessary administration from avoidable complexity.
We should question recurring problems.
We should demand evidence from technology vendors.
We should use experienced professionals where their expertise matters most.
And we should design systems that prevent mistakes whenever reasonably possible.
Because the greatest healthcare innovation may not be the technology that fixes the problem fastest. It may be the system that makes the problem unnecessary.
The patient deserves timely care.
The physician deserves a sustainable practice.
The staff deserves workflows that make sense.
And the healthcare system deserves a better ambition than automating the same argument forever.
Stop building better mops. Fix the leak.
Get Involved: The Next Healthcare Breakthrough Starts With an Uncomfortable Question
If you could permanently eliminate one administrative problem from your medical practice, what would it be — insurance denials, prior authorization, documentation overload, or delayed payments?
Share your answer in the comments. Physicians, clinic owners, billing professionals, and healthcare leaders all see different parts of the same problem, and your experience may help others rethink what they’ve accepted as normal.
If this perspective resonates, repost this article. Let’s bring more independent physicians into a conversation about preventing problems instead of building an entire industry around repairing them.
About the Author
Dr. Daniel Cham, MD, is a physician, healthcare entrepreneur, author, and medical consultant whose work focuses on healthcare technology, medical practice management, and the intersection of clinical care and financial operations.
He is the founder of OnnX, an AI-powered medical billing software initiative exploring ways to reduce administrative friction, improve information quality, and strengthen revenue-cycle processes for independent physician practices.
His writing combines clinical perspectives, healthcare operations, emerging technology, and practical lessons for professionals navigating a changing medical environment.
Continue the Conversation: Better Questions Create Better Healthcare
The most useful conversations often begin by challenging assumptions that everyone else has stopped questioning.
For more perspectives on healthcare, artificial intelligence, entrepreneurship, medical practice management, and lessons from everyday life, explore my work across these platforms.
Connect: LinkedIn | Website | YouTube| Spotify
Knowledge doesn’t create progress until someone questions the status quo and acts on what they learn. Start asking better questions. The next improvement may begin with yours.
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References and Further Reading
1. AI Helps Orthopedic Practice Reduce Surgical Coding Costs — Healthcare IT News
Examines how an orthopedic practice reported an 80% reduction in surgical coding costs using AI-assisted revenue-cycle technology, highlighting the potential for targeted automation to reduce administrative expenses.
2. AI Scribes, Revenue Gaps, and Financial Conversations — Medical Economics
Explores the intersection of clinical documentation, hidden revenue leakage, and practice financial management, reinforcing the need to address administrative inefficiencies before they affect reimbursement.
3. Why Paid Medical Claims Can Still Hide Underpayments — MarketScale
Highlights why claim acceptance and payment do not necessarily guarantee correct reimbursement, emphasizing contractual payment verification and revenue integrity.
Related Articles by Dr. Daniel Cham
4. Your Billing Denial Has a Plot Twist: The Problem May Have Started Before the Claim Existed
Explores how eligibility errors, missing authorizations, documentation gaps, and disconnected workflows can cause denials long before claims are submitted. Supports the argument for upstream denial prevention.
5. The Cheapest Claim Is the One You Never Have to Fix
Examines why preventing avoidable claim errors may be more valuable than improving appeal speed. Discusses rework costs, denial rates, payment delays, and revenue-cycle efficiency.
Challenges the assumption that better billing software alone can fix reimbursement problems. Focuses on root-cause analysis, workflow redesign, administrative waste, and practical improvements for independent clinics.
Authoritative Research and Regulatory Guidance
7. American Medical Association — Prior Authorization Survey Findings
Provides physician-reported findings on prior authorization workload, treatment delays, patient safety concerns, and physician burnout, supporting the article’s discussion of administrative burden.
8. CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F)
Outlines federal requirements intended to improve health information exchange, prior authorization transparency, and electronic workflows for affected payers, with several implementation requirements beginning in 2027.
Professional Disclaimer
This article is intended for general education and professional discussion. It is not individualized medical, legal, financial, coding, or regulatory advice. Healthcare organizations should consult qualified professionals and review current laws, payer policies, and contractual obligations before implementing changes.
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