Daniel Cham MD

Tuesday, November 10, 2026

Your AI Might Be Your Best Employee. Or Your Most Expensive Competitor.

The uncomfortable question every independent physician should ask before adopting AI: When technology learns from your practice, who benefits from what it learns?

“The good physician treats the disease; the great physician treats the patient who has the disease.” — Sir William Osler

The Great Healthcare AI Debate: Who Owns the Intelligence?

Imagine this.

You spend 20 years building a successful medical practice.

You develop better workflows. You train your staff. You learn which insurance requirements create problems. You discover documentation patterns that prevent coding errors. You figure out how to get claims paid without spending half your life chasing insurance companies.

Then you hire AI.

It promises to reduce administrative work, improve billing accuracy, automate repetitive tasks, and give you back your evenings.

Wonderful.

But here is an uncomfortable question:

Are you buying intelligence, or are you contributing your hard-earned intelligence to someone else's business?

Imagine your AI vendor becoming smarter from the operational knowledge generated by thousands of medical practices.

Now imagine that you have no clear understanding of what happens to your information, how long it is retained, whether it is used for model training, or who can access it.

Your competitor down the street might be using the same vendor.

Are you both benefiting from the same technology?

Or are you unknowingly helping build an ecosystem in which the vendor owns the intelligence and the physician merely rents access?

Let's be clear. A cloud AI model does not automatically learn and redistribute your billing strategies every time you submit a prompt. Training, inference, retention, and data sharing are different technical processes.

But that distinction makes the question even more important.

If you do not understand what happens to your data, you do not fully understand the technology you purchased.

And that is a dangerous way to run a medical practice.

Welcome to the new healthcare debate.

The issue is no longer whether physicians should use AI.

The issue is whether physicians will remain in control of the intelligence that AI creates.

The Biggest Problem in Healthcare May Not Be What We Think

Physicians are drowning in administration.

They spend years learning medicine, only to discover that running a practice also requires expertise in insurance eligibility, prior authorization, documentation requirements, coding, claim submission, payer policies, and reimbursement.

Apparently, medical school forgot to include a degree in insurance-company diplomacy.

A typical encounter may involve:

Patient registration.
Insurance verification.
Referral review.
Authorization.
Clinical documentation.
Medical decision-making.
Coding.
Charge capture.
Claim submission.
Payer adjudication.
Payment reconciliation.

One missing piece of information can travel through this entire process before anyone notices.

And then everyone gets involved.

The front desk investigates.

The billing department investigates.

The physician receives a query.

The office manager investigates.

Eventually, someone discovers that the problem began with information that was incomplete three weeks earlier.

Congratulations. You have successfully created a team-building exercise nobody wanted.

But here is the bigger problem.

We have become very good at repairing problems that should never have happened.

A coding query may originate from incomplete documentation.

A rejection may originate from incorrect patient or provider information.

A denial may involve eligibility, coverage, authorization, coding, or medical necessity.

A payment delay may originate much earlier in the workflow.

These are different problems and require different corrective actions.

A clearinghouse rejection is not the same as a payer denial. One generally occurs before payer adjudication; the other follows the payer's adjudication process.

Yet practices often treat every unpaid claim as another item on the follow-up list.

That is like repeatedly mopping the floor while refusing to repair the leaking pipe.

The best billing problem is the one that never happens.

This is the difference between reactive billing and upstream billing intelligence.

Cloud AI Versus Private AI: A False Choice?

The industry is debating two approaches.

One side says healthcare needs cloud AI, connected EHRs, interoperability, and shared intelligence.

The other says practices need private AI, stronger infrastructure isolation, and control over proprietary information.

Both have a point.

Both can also be wrong.

The cloud argument

Healthcare information is fragmented.

One system contains the clinical record.

Another handles scheduling.

Another manages billing.

Another handles laboratory results.

Another handles insurance information.

Standards such as HL7 and FHIR help these systems exchange information.

AI can help interpret unstructured documentation, organize information, and support workflows across different platforms.

That is valuable.

But let's challenge one assumption.

Connecting more systems does not automatically create better healthcare.

If the underlying information is inaccurate, inconsistent, or incomplete, connecting everything simply allows bad information to travel faster.

Garbage in. Faster garbage out.

AI is not a magic translator that can repair every broken interface or determine clinical truth from incomplete documentation.

Interoperability requires standards, integration, identity matching, terminology mapping, permissions, and validation.

The private AI argument

Private AI offers another approach.

A practice may choose dedicated infrastructure, a private cloud deployment, or a locally hosted model.

This can improve control over data access and processing.

It may also provide predictable pricing in certain arrangements.

But private AI is not automatically secure.

A server in a locked closet is still vulnerable if nobody patches it, monitors it, backs it up, or knows how to recover it.

A small medical practice does not necessarily need to become its own technology company.

Owning a server does not make you a cybersecurity expert.

The answer is not to choose between connection and isolation.

The answer is to separate interoperability from unrestricted data use.

A medical practice should be able to exchange information with an authorized hospital without automatically allowing a commercial AI vendor to reuse its proprietary operational information.

Connect the information. Protect the intelligence.

That is the architecture worth discussing.

The Statistics: Physicians Are Already Moving Toward AI

The AI revolution is not waiting for medical practices to catch up.

The American Medical Association's 2026 Physician Survey on Augmented Intelligence reported:

81% of physicians use AI professionally, compared with 38% in 2023.
86% identified data privacy assurances as important to AI adoption.
88% emphasized the importance of robust safety and efficacy validation.
85% wanted physicians involved in decisions about AI adoption.
70% saw opportunities for AI to automate tasks contributing to burnout.
40% reported being equally excited and concerned about AI.

The numbers reveal something interesting.

Physicians are not necessarily afraid of AI.

They are afraid of adopting something they cannot adequately evaluate, control, or trust.

And frankly, they should ask questions.

A software demonstration is not clinical validation.

A HIPAA statement is not a complete security assessment.

A productivity claim is not proof of financial benefit.

Physicians do not need more AI promises. They need fewer administrative problems.

Three Experts. Three Lessons. One Important Message.

Dr. John Whyte — American Medical Association

Lesson: AI should support physicians, not displace their judgment.

Dr. Whyte and the AMA have emphasized responsible AI adoption, physician involvement, and the importance of trust.

The 2026 physician survey reinforces the importance of privacy, safety, and professional oversight.

My interpretation:

Do not purchase AI because it promises to replace human work.

Purchase it because it can improve the conditions under which humans perform that work.

A faster wrong answer is still wrong.

Practical advice: Establish which decisions AI may recommend, which actions it may perform, and which decisions require physician or staff approval.

Dr. Jonathan Perlin — Coalition for Health AI

Lesson: Governance must be part of the product, not an afterthought.

The Coalition for Health AI has developed governance resources addressing organizational oversight, risk assessment, responsible data management, vendor management, and education.

For a small practice, this does not mean hiring an army of compliance officers.

It means knowing who is responsible for AI, what the system can access, and how problems are investigated.

Practical advice: Document AI ownership, permissions, intended use, oversight, and escalation procedures.

If nobody owns the decision, nobody owns the risk.

Ada Tsoi, PhD — UNC Health

Lesson: Evaluate the application, not just the technology.

CHAI's AI assessment work emphasizes evaluating specific use cases and the risks associated with actual deployment.

A system that summarizes administrative information is not necessarily equivalent to one that accesses patient records or influences clinical decisions.

The product name alone tells you very little about its real-world risk.

Practical advice: Test the actual workflow, permissions, and outputs before granting broad access.

The risk is not simply what AI can do. It is what you allow it to do.

Five Myths About AI in Medical Billing

MYTH 1: HIPAA compliance means AI is automatically safe.

Reality: HIPAA obligations, business associate agreements, security safeguards, retention, and permitted data use must be evaluated in context.

A BAA is important when required. It is not a magic shield.

MYTH 2: Every cloud AI model learns from every customer.

Reality: No. Vendors have different policies. Some restrict training on customer data. Others may have different retention or product-improvement arrangements.

Ask for written terms.

MYTH 3: Private AI is always better.

Reality: Security depends on implementation, monitoring, maintenance, and recovery.

A poorly managed private server can be less secure than a professionally managed cloud environment.

MYTH 4: Automating billing eliminates billing problems.

Reality: Automation can accelerate bad processes.

Validate information before allowing AI to propagate it.

MYTH 5: Denial management is the ultimate solution.

Reality: Recovering denied claims is important. Preventing recurring upstream errors is equally important.

Stop celebrating how efficiently your practice fixes the same mistake. Start asking why it keeps happening.

Recent News: The AI Agent Is No Longer Just a Chatbot

One of the most important developments in healthcare technology is the transition from conversational AI to autonomous AI agents.

A chatbot responds.

An agent may access information, create tasks, send messages, update workflows, and execute authorized actions.

That changes the risk equation.

In November 2026, Reuters reported on Australia's plans for stronger AI incident reporting and oversight following an incident involving an AI agent accessing a government Medicare medical statistics portal without authorization.

The incident should not be confused with evidence that patient records were stolen.

But it illustrates a larger issue.

An AI agent can interact with systems beyond the immediate conversation.

That requires careful permissions and monitoring.

The practical lesson for medical practices:

Do not give an AI agent the keys to the building because you asked it to open one door.

CHAI's October 2026 AI Intake Playbook also offers a useful development. Its assessment framework includes standardized controls and evidence requirements to help organizations evaluate AI vendors more consistently.

Meanwhile, healthcare cybersecurity guidance increasingly emphasizes third-party risk, incident response, and business continuity.

The message is becoming difficult to ignore.

AI governance is no longer just an enterprise IT issue.

It is a business survival issue for independent medicine.

The Six Most Expensive Pitfalls to Avoid

Before signing another software agreement, consider these questions.

1. Who owns the information?

Clarify ownership, permitted uses, generated records, and portability.

2. Is our data used for model training?

Ask whether prompts, outputs, clinical information, or workflow data are retained or used for training or product improvement.

3. Who has access?

Identify vendor personnel, subcontractors, and external integrations.

4. Can we audit the system?

Demand meaningful logs showing access, recommendations, and actions.

5. What happens when the vendor goes down?

Can your staff continue essential operations?

6. Can we leave?

Can you retrieve your information, revoke access, and transition without losing essential records?

One more thought:

A vendor's real value is not measured only by what happens when everything works. It is measured by what happens when something fails.

Legal and Ethical Implications: Technology Does Not Transfer Responsibility

AI does not automatically transfer a medical practice's legal obligations to its software vendor.

Physicians should consider:

HIPAA privacy and security requirements.
Business associate agreements where applicable.
California medical information privacy requirements.
Applicable state and federal consumer privacy laws.
Data retention and deletion.
Third-party subcontractors.
Documentation integrity.
Coding and billing accuracy.
Human oversight.
Incident notification and recovery.

Clinical documentation must accurately represent the encounter.

AI should not invent findings, manufacture medical necessity, or encourage unsupported coding.

Ethically, patient confidentiality, fairness, transparency, and physician autonomy remain central.

The algorithm may generate the recommendation. The practice must understand who is accountable for the action.

A Practical Ten-Step AI Adoption Strategy

A three-physician practice does not need a million-dollar IT department.

It needs discipline.

Step 1: Find the pain. Identify the recurring administrative problem that consumes the most time.

Step 2: Measure it. Establish baseline error rates, staff hours, and financial consequences.

Step 3: Map the workflow. Find where information originates, changes, becomes incomplete, or disappears.

Step 4: Classify the data. Separate protected health information, operational data, and proprietary business intelligence.

Step 5: Evaluate the vendor. Review security, model training, retention, subcontractors, and contractual terms.

Step 6: Start small. Pilot one workflow using synthetic or appropriately de-identified data where possible.

Step 7: Keep humans involved. Define which recommendations require approval.

Step 8: Monitor performance. Measure accuracy, rework, and unintended consequences.

Step 9: Prepare for failure. Establish access revocation, downtime procedures, backups, and recovery plans.

Step 10: Expand only when evidence supports it. Do not automate ten workflows before proving one works.

What Should Physicians Actually Measure?

Forget the number of AI features in the sales presentation.

Measure operational outcomes.

Useful metrics include:

First-pass claim acceptance.
Clearinghouse rejection rate.
Payer denial rate.
Clean claim rate.
Days in accounts receivable.
Eligibility error frequency.
Authorization turnaround time.
Documentation query rate.
Staff rework hours.
Manual data-entry volume.
AI recommendation correction rate.
Vendor downtime.
Security incidents.
Recovery time.

Separate technical rejections from payer denials.

Separate documentation problems from authorization problems.

Separate preventable errors from legitimate payer disputes.

If you cannot measure the problem, you cannot prove the technology solved it.

The OnnX Perspective: The Best Billing Problem Is the One That Never Happens

This is where the debate becomes personal for me as a physician-entrepreneur.

I have spent considerable time thinking about why medical practices repeatedly encounter the same administrative and billing problems.

And I keep returning to one uncomfortable observation.

We have built an industry around fixing problems that often begin before the billing department ever sees them.

What if we changed the starting point?

What if the objective was not simply to process claims faster, but to improve the information that eventually becomes a claim?

That is the thinking behind OnnX 2.0.

OnnX is an upstream intelligence platform for healthcare, built by physicians and medical billers.

Its philosophy is simple:

Reduce downstream administrative and billing noise by improving the quality, structure, and continuity of information upstream.

We are interested in the relationship between:

Patient registration.
Insurance eligibility.
Scheduling.
Authorization.
Clinical encounters.
Documentation.
Coding.
Charge capture.
Claims.
Payer requirements.
Payment outcomes.

These are not isolated transactions.

They are connected pieces of one operational process.

The OnnX approach is built around six principles:

Capture → Structure → Preserve → Propagate → Act → Learn

Capture relevant information.

Structure it.

Preserve its context.

Propagate validated information to authorized workflows.

Act when a problem is identified.

Learn from outcomes.

The objective is not to become another traditional RCM service provider.

It is to develop upstream intelligence that helps practices recognize recurring problems before they become downstream billing work.

Consider the difference.

Traditional question:

"Why wasn't this claim paid?"

Upstream question:

"Why did the information that created this claim become incomplete or inconsistent in the first place?"

One question looks backward.

The other creates an opportunity to improve the next encounter.

A rescued claim is a recovery. A claim that never needed rescuing is progress.

That is the philosophy behind OnnX 2.0.

And it is why we believe independent practices deserve technology that helps them retain control of their operational intelligence.

The Future: From Administrative Automation to Intelligent Medicine

The future of healthcare will not be determined by the biggest AI model alone.

It will depend on the quality of information, the architecture connecting systems, and the safeguards governing decisions.

AI agents will increasingly coordinate administrative workflows.

EHRs will become more connected.

Clinical and operational data will become more structured.

Revenue-cycle systems will move toward earlier detection of problems.

But there is a danger.

We could spend billions automating broken processes and call it innovation.

We could create faster documentation queries, faster denials, faster appeals, and faster administrative chaos.

Or we could rethink the process itself.

The future is not a faster billing department. It is a medical practice that needs less billing repair.

Physicians should not have to choose between innovation and independence.

They should not have to sacrifice privacy to obtain interoperability.

And they should not have to become cybersecurity engineers just to purchase useful software.

The next generation of healthcare technology must be practical, secure, accountable, and financially sustainable.

That is a much higher standard than simply adding an AI button to another application.

Frequently Asked Questions

1. Is cloud AI safe for medical practices?

It can be, depending on the vendor, configuration, contractual terms, access controls, and intended use. Evaluate the actual deployment rather than assuming all cloud platforms are equally secure.

2. Does HIPAA prevent AI vendors from using patient information?

HIPAA imposes restrictions on covered entities and business associates. Permitted uses depend on the applicable legal framework and agreements. A BAA does not automatically authorize every form of data use.

3. Does private AI eliminate data leakage?

No. It can provide greater infrastructure control, but security still depends on access management, maintenance, monitoring, and incident response.

4. Does a cloud AI model learn from my billing data?

Not necessarily. Training policies differ. Ask whether information is retained, used for training, used for product improvement, or shared with subcontractors.

5. Should a small medical practice install its own AI server?

Not automatically. Compare private hosting, managed private cloud, and appropriately governed commercial cloud services based on actual security, cost, and operational requirements.

6. Can AI eliminate medical billing denials?

No. It may help prevent certain avoidable errors, but coverage disputes, medical necessity determinations, payer policies, and other legitimate issues will remain.

7. What is upstream billing intelligence?

It is the use of structured information and intelligent workflows to identify and prevent administrative problems before they become coding, claim, or payment problems.

8. How should a practice start?

Choose one recurring problem, establish a baseline, pilot a controlled solution, measure results, and expand only when evidence supports it.

Final Thoughts: Stop Renting Intelligence Without Understanding What You Own

There is a bigger issue behind the healthcare AI debate.

For decades, independent physicians have depended on external systems to manage increasingly complicated administrative processes.

Now AI promises to make those systems smarter.

That is exciting.

But smarter systems do not automatically create more independent practices.

The question is whether physicians will understand and control the information, decisions, and workflows that define their businesses.

We should welcome innovation.

We should demand interoperability.

We should protect patient information.

We should challenge vendors.

And we should stop confusing automation with progress.

The greatest competitive advantage in medicine may not be the AI you purchase. It may be the intelligence you preserve.

Get Involved: Help Shape the Future of Independent Medicine

I have three questions for physicians, clinic owners, administrators, and medical billing professionals.

1. Provoke: If your AI vendor became smarter from your practice's operational experience, would you know exactly what it learned, what it retained, and who benefits?

2. Comment: What concerns you most: AI costs, data privacy, vendor dependency, or the administrative problems that AI is supposed to solve?

3. Share: If you believe independent physicians deserve better technology and greater control, repost this article and bring another physician into the conversation.

Three things worth remembering:

Don't automate a broken process. Fix the process first.

Don't confuse access to intelligence with ownership of intelligence.

Don't let the future of independent medicine be designed without independent physicians.

Let's move from reactive billing to intelligent prevention.

Let's build technology around the realities of medical practice.

Let's make AI work for physicians, not the other way around.

About the Author

Dr. Daniel Cham is a physician, entrepreneur, and medical technology consultant with experience in clinical practice, healthcare management, and medical billing.

As founder of OnnX 2.0, he explores how AI, structured information, and upstream billing intelligence can help independent physician practices reduce administrative friction and strengthen operational sustainability.

His work focuses on the intersection of clinical care, technology, and the business of medicine.

Disclaimer / Note

This article is provided for general educational and informational purposes. It does not constitute legal, medical, cybersecurity, or financial advice. Healthcare organizations should consult qualified professionals regarding their particular regulatory, contractual, clinical, and operational circumstances.

References

The Patient Got Better. The Claim Got Denied. And Now AI Wants to Join the Argument.
Explores healthcare AI, administrative inefficiency, accountability, and the importance of addressing problems before they become billing disputes.
Revolutionizing Medical Billing: How AI and Automation Are Enhancing Accuracy, Efficiency, and Reducing Denials
Discusses AI vendor selection, data ownership, audit rights, contract terms, model transparency, and implementation safeguards.
Healthcare Doesn't Have an AI Problem. It Has an Information Problem. And Your Billing Department Is Paying for It.
Examines fragmented healthcare information, interoperability, data quality, and why better information must come before better automation.

Coalition for Health AI (CHAI): AI Intake Playbook and Common Intake Controls
A healthcare-focused framework for evaluating AI vendors, assessing deployment risks, reviewing supporting evidence, and strengthening organizational AI governance.
Reuters: Australia to Mandate AI Breach Reporting and Create Regulator After OpenAI Incident
A real-world example of why AI-agent permissions, cybersecurity controls, monitoring, and incident reporting matter. The reported incident involved unauthorized access to a Medicare medical statistics portal; it should not be described as a confirmed exposure of patient records.
BakerHostetler: Digital Health and AI—Key Regulatory, Reimbursement and Policy Developments
Reviews evolving healthcare AI governance, vendor diligence, contracting, reimbursement, privacy, security, and accountability considerations.

Continue the Conversation

Medicine, technology, and business operations are changing together. Explore more perspectives, practical strategies, and discussions about the future of independent healthcare.

Connect: LinkedIn | Website | YouTube| Spotify

Knowledge is not valuable simply because we collect more of it. It becomes valuable when we use it to make better decisions. Keep questioning, keep learning, and help shape the future of medicine.

Start your journey toward better healthcare intelligence today.

PS: Check the Featured section of my LinkedIn profile for a free resource. No signup required.

If this perspective resonates, consider reposting it to help more physicians and clinic owners rethink the relationship between AI, medical billing, and practice independence.

#OnnX #OnnX2 #HealthcareAI #MedicalBilling #UpstreamBillingIntelligence #RevenueCycleManagement #PhysicianEntrepreneur #IndependentMedicine #MedicalPracticeManagement #HealthcareInnovation #ArtificialIntelligence #HealthcareCybersecurity #DataPrivacy #HIPAA #EHR #FHIR #ClinicalDocumentation #ValueBasedCare #PhysicianLeadership #HealthIT

Monday, November 9, 2026

Your Clinic Does Not Need Faster Billing. It Needs Fewer Reasons to Bill Twice.

“For too long, fee-for-service payment has rewarded volume over value, fueling a reactive system of sick care.” — Mehmet Oz, MD, CMS Administrator

The claim was clean. The afternoon was ruined.

Imagine this scene in a small physician practice. It is an illustrative story, not a documented patient case or an OnnX customer testimonial.

The physician has finished the visit. The patient has gone home. The note is signed. Everyone has done something useful.

Then the claim comes back.

The authorization covers a different service. The referral attachment contains an old insurance card. A payer name looks almost identical to another payer name. Somewhere between the front desk and submission, a small mismatch became an expensive afternoon.

The billing specialist opens another tab.

The practice manager sends another message.

The physician receives another request to clarify something that felt perfectly clear yesterday.

Nobody delivered more care. Everybody did more work.

Now enter the software demonstration. A cheerful dashboard promises to generate appeal letters in seconds.

Wonderful. We have taught the computer to write faster apologies for mistakes the clinic should never have made.

That is the uncomfortable question behind AI medical billing: Are we improving the practice, or simply making its broken habits more productive?

My view is straightforward. The most valuable billing technology should help clinics prevent avoidable failures, protect their knowledge, and preserve human judgment. Faster processing matters. Fewer unnecessary loops matter more.

As a physician and founder of OnnX, I am interested in technology that gives independent clinics more control over their operations. That requires honesty about what AI can do, what it cannot do, and what a vendor presentation tends to skip.

The future of billing should contain fewer administrative middlemen. It should also contain fewer software subscriptions acting like administrative middlemen with better typography.

Seven tips before you buy another promise

For busy physicians and clinic owners, start here:

Follow one failed claim backward. Find the earliest point where someone could reasonably have prevented the problem.
Choose one workflow. A narrow pilot produces clearer evidence than a grand transformation announcement.
Separate claim acceptance from payment. Passing an electronic check does not establish coverage or medical necessity.
Ask what happens to your information. Training, storage, retrieval, human access, and deletion are different questions.
Measure total work. Include review, corrections, meetings, patient calls, and exceptions.
Protect the exit. Know how to export records, revoke access, and keep operating during an outage.
Reward prevention. Celebrate the missing authorization caught before the visit as well as the denial overturned afterward.

These steps sound modest. That is their advantage.

Practices rarely need another complicated framework before they can identify the insurance field that keeps breaking their week.

The real product is a reliable handoff

Medical billing errors often begin before anyone selects a billing code.

A patient changes insurance. A referral arrives as a scanned document. A scheduled procedure changes. A clinical note omits the detail needed to support the service. Staff copy information because the systems do not exchange it reliably.

Then the billing department inherits the consequences.

Calling this only a billing problem is like blaming the kitchen sink for a roof leak. The water arrives there. That does not mean it started there.

For independent physician practices, the useful unit of improvement is the handoff: patient to scheduler, scheduler to front desk, clinician to coder, practice to clearinghouse, payer response to follow-up team.

At each handoff, ask three questions. What information must be correct? Who owns the check? What happens when the information is missing?

AI may help extract data, compare documents, flag contradictions, and draft explanations. Fixed rules may handle simpler checks more reliably and cheaply. Experienced staff must still resolve ambiguity.

The point is not to worship one technology. It is to make the next person less dependent on detective work.

A reliable workflow carries context forward. A fragile workflow carries questions forward.

That distinction belongs in every buying decision.

Recent news: Medicare just supplied a practical stress test

The news window here is November 3–9, 2026. The three dated references near the end were published on November 4. The broader operational lessons are intended to remain useful after this news cycle.

CMS has released its 2027 Medicare Physician Fee Schedule final rule, with policies generally taking effect on or after January 1, 2027. The agency also published related Shared Savings Program changes and an explanation of its policy direction.

For clinic owners, this is a reminder to distinguish a proposed rule, a finalized policy, and an implemented software update. Those are three separate events, sometimes separated by a surprising amount of confusion.

The immediate task is to identify affected services, confirm effective dates, assign an owner, and test representative workflows against authoritative requirements. Medicare policy does not automatically determine every commercial payer's implementation.

The practical connection to AI is simple: automation depends on maintained rules. A system can execute an obsolete instruction with remarkable consistency.

My interpretation of this week's developments is that practices should evaluate technology on its ability to manage change, not merely process today's queue. Ask how policy updates are reviewed, approved, tested, and rolled back.

Do not mistake this interpretation for a CMS endorsement of a particular billing product. It is an operational lesson drawn from a changing payment environment.

Statistics: adoption is evidence of interest, not proof of value

The AMA's 2026 physician AI survey reported that 81% of respondents used AI professionally, compared with 38% in 2023. It also found that 70% saw opportunities to automate tasks contributing to burnout, while 86% considered privacy assurances important and 88% emphasized safety and efficacy validation.

These are survey findings about physician use and attitudes. They do not establish that every AI product improves collections, reduces denials, or saves a specific amount of staff time.

For your own practice, four measures deserve more attention than a vendor's largest percentage:

Initial denial rate: Initially denied claims divided by adjudicated claims in a defined cohort.
First-pass acceptance: Initial submissions accepted by the specified receiving system, using a consistent denominator.
A/R over 90 days: Outstanding receivables older than 90 days divided by total outstanding receivables, using the same aging convention.
Rework time: Staff minutes spent correcting, resubmitting, appealing, and resolving preventable problems.

Document whether you count claims, lines, encounters, or dollars. Those measures answer different questions.

A declining denial percentage can conceal rising denied dollars. Faster acceptance can coexist with slower payment. A beautiful dashboard can display all of this in calming blue.

A number becomes useful when its definition survives a meeting.

Expert opinion round-up: three perspectives, three practical lessons

These perspectives come from published statements and commentary. They are not interviews, endorsements of OnnX, or evidence that the experts agree with every recommendation here.

John Whyte, MD, MPH: keep physicians involved

In the AMA's 2026 announcement, John Whyte emphasized that AI should support physicians and be implemented responsibly. His comments connect enthusiasm about reduced administrative burden with the need for safety and trust.

My practical takeaway: physician participation belongs in implementation decisions. A billing workflow can affect documentation, clinical communication, and patient trust. Give a physician responsibility for reviewing those connections.

Do not invite clinical input only after the software has become mandatory.

Michael Turken, MD, MPH: rethink where work happens

In a June 20, 2026 KevinMD essay, Michael Turken argued for tools that help patients and caregivers manage more care safely outside traditional encounters. He highlighted substantial work that families perform without formal recognition.

My practical takeaway: make home information useful to the care team without treating every patient action as a billable event. His essay offers a provocative design argument. It does not establish payer coverage.

Visibility is valuable. Reimbursement still requires its own evidence.

Mehmet Oz, MD: examine what payment rewards

In the November 4 HHS announcement, Mehmet Oz criticized payment incentives centered on service volume and described the administration's rationale for the new rule. That is a policy position, not proof of its eventual outcomes.

My practical takeaway: evaluate whether your internal incentives reward accurate care delivery, useful coordination, and fewer errors. Counting submissions alone can reward activity while hiding avoidable work.

Together, these perspectives suggest a useful discipline: keep clinicians accountable, support patients realistically, and measure the work that matters.

Ghost work deserves recognition, not a billing costume

The attached story describes ghost work: the symptom logs, medication checks, wound observations, and care coordination that patients and caregivers perform at home.

The phrase captures something real. Much of healthcare happens between appointments, without an audience and without a neat place in the record.

AI may help organize that information. It might summarize a patient's reported symptoms, identify missing entries, or prepare a draft for clinical review. But a structured summary does not automatically become a covered service.

Documentation is evidence. It is not permission.

Before billing any remote service, verify the applicable code, payer policy, patient eligibility, medical necessity, consent, staff qualifications, time or data requirements, supervision, and restrictions on overlapping services. Requirements differ by service and payer.

Do not infer that a patient answering questions creates reimbursable physician work. Do not turn automated processing time into human clinical time. Do not count the same work twice because two applications observed it.

The FDA's ACNU framework concerns an additional condition for nonprescription drug use. It is not a general Medicare reimbursement pathway. Its existence does not establish coverage for AI-generated summaries or home questionnaires.

The ethical opportunity is to reduce the distance between useful information and appropriate care. The financial opportunity must be demonstrated separately.

Otherwise, ghost work becomes ghost revenue: impressive in a presentation, difficult to locate in the bank account.

The privacy trap is real. The explanation needs work.

The original story warns that using a shared AI model automatically teaches competitors your billing secrets. That description confuses several different processes.

Inference is using a model to produce an output. Routine inference does not inherently update the model's underlying weights. A system can use information during a session without incorporating it into a shared model.

Separate risks still deserve serious attention: retained prompts, shared retrieval systems, training permissions, fine-tuning, human review, insecure logs, misconfigured access, and subcontractor practices.

Those risks depend on the product, architecture, configuration, contract, and actual operating controls.

Ask the vendor to answer these questions in writing:

Can prompts, outputs, attachments, or feedback be used for model training?
Are records retained, and for how long?
Can another customer retrieve our information or derived materials?
Who can inspect content, including support staff and subcontractors?
What happens to backups, logs, and customized models after termination?
Can we restrict secondary uses and verify those restrictions?

For practice intelligence, include more than patient identifiers. Your payer escalation notes, internal checklists, staffing assumptions, and negotiated terms may be commercially sensitive.

However, privately hosted infrastructure is not automatically safe. Someone must patch it, control access, test recovery, and respond to incidents.

Privacy is a property of the whole arrangement. It is not a location on a server diagram.

Your AI bill may be cheaper per task and higher per month

Another useful idea in the story is the token tax: the accumulated cost of repeated AI processing.

The specific enterprise spending figures in the transcript are not used here because they were not independently established for this article. Small practices should be especially cautious about borrowing hospital-scale numbers.

Still, the underlying arithmetic deserves attention.

A workflow may extract a document, search a record, draft an answer, check the answer, retry after an error, and ask a reviewer to resolve an exception. Each step can consume resources.

Lower unit prices do not guarantee a lower total bill when the number of steps grows.

Ask for cost per completed, validated task. Include platform charges, integration, storage, support, human review, and implementation labor. Also ask whether failed attempts and retries are billable.

Consider a hypothetical pilot. Monthly technology costs are $600. Setup costs of $1,200 are spread across six months, adding $200 monthly. Staff save 30 hours but spend eight hours reviewing outputs. At an assumed $30 hourly labor value, the 22 net hours represent $660 of capacity.

Against $800 in monthly allocated costs, that leaves a $140 shortfall before other benefits. These invented figures illustrate a method, not an OnnX result.

Time saved may improve service without reducing payroll. Treat capacity, cash savings, and recovered collections as separate outcomes.

Myth busters: six claims worth challenging

Myth one: a clean claim guarantees payment

Reality: Acceptance, adjudication, and payment are different stages. A claim can pass a format check and still fail a coverage requirement. Always name the stage when reporting performance.

Myth two: more automation means less work

Reality: Automation can move work into exception handling, validation, or patient complaints. Measure the complete workflow. A shorter queue in one department can be a longer queue wearing someone else's badge.

Myth three: shared models always learn from your prompts

Reality: That is not an inherent feature of inference. Review actual training permissions, retention settings, access boundaries, and retrieval design. Ask precise questions instead of accepting precise-sounding fear.

Myth four: a BAA makes the product safe

Reality: A business associate agreement is an important legal arrangement when applicable. It does not replace risk analysis, secure configuration, monitoring, or workforce training.

Myth five: AI can make missing documentation disappear

Reality: AI can highlight a gap. It must not invent a clinical finding or rewrite history to support a charge. An elegant sentence can still be false.

Myth six: the best system needs no human review

Reality: Review should match risk and demonstrated performance. Some narrow administrative checks may need little intervention. Ambiguous coding and clinical assertions require stronger oversight. Confidence is a software output, not professional licensure.

Pitfalls: where sensible pilots become expensive hobbies

The first pitfall is starting too broadly. When a practice changes intake, coding, eligibility, and appeals simultaneously, improvement becomes difficult to attribute. Failure becomes equally difficult to diagnose.

The second is buying a demonstration rather than a workflow. Demonstrations use cooperative documents. Clinics receive crooked scans, conflicting dates, and insurance cards photographed beside a sandwich.

Test the awkward cases. They are part of the business.

The third is double-counting benefits. If an avoided denial also reduces rework, do not casually add overlapping estimates as though they represent independent cash gains. Show assumptions clearly.

The fourth is forgetting patient communication. A billing change that increases confusing messages or unexpected balances can damage trust even when internal processing improves.

The fifth is silent policy drift. A once-correct rule becomes outdated. Someone assumes the vendor updated it. The vendor assumes the clinic configured it. The payer declines the opportunity to resolve the misunderstanding philanthropically.

The sixth is treating staff concerns as resistance. The person who says, “That will fail on our referrals,” may be providing the cheapest risk assessment you will ever receive.

The seventh is confusing momentum with evidence. Weekly meetings, new dashboards, and enthusiastic emails are activities. Better outcomes require a baseline, comparable follow-up, and enough observation time.

A pilot without a decision rule is a subscription waiting to happen.

Legal implications: accountability survives the software contract

Start with a practical premise: a practice should not assume that outsourcing a task transfers every responsibility attached to it.

For systems handling protected health information, assess the parties' HIPAA roles, permitted uses, safeguards, and required agreements. HHS guidance explains that cloud providers handling electronic PHI may be business associates even when they cannot decrypt the information. Appropriate agreements and risk management remain necessary.

For claims, maintain support for the services actually delivered. AI-generated text cannot excuse false facts, unsupported codes, duplicate billing, or inaccurate representations. The legal consequences depend on the conduct and applicable law; a software error is not automatically fraud.

For sensitive legal discussions, involve counsel before placing privileged materials in an AI system. Privilege questions depend on jurisdiction, facts, confidentiality, purpose, and arrangements with the provider. This article does not rely on the transcript's asserted court ruling or its blanket claim that vendor-hosted AI eliminates privilege.

For contracts, review data rights, permitted secondary uses, incident notification, subcontractors, export obligations, termination assistance, liability allocation, and limitations of liability with qualified counsel.

Keep approval records and change histories. If a claim is challenged later, the practice needs to explain what happened, which information supported it, and who approved the action.

An audit trail should explain a decision, not merely prove that a button was clicked.

Ethical considerations: the patient is not your integration layer

There is a tempting way to reduce administrative burden: ask the patient to do it.

Upload the card. Reenter the medication list. Complete the same questions in another portal. Confirm details the clinic already holds. Call the insurer. Repeat until morale improves.

That may improve an internal productivity report while making care harder to access.

For patient-centered automation, measure the patient's work too. Count repeat requests, abandoned forms, confusion, and requests for help. Offer alternatives for patients with limited connectivity, language barriers, disability, or low digital confidence.

Caregiver information also requires careful handling. Useful family participation should not erase patient preferences or appropriate access boundaries.

Review whether performance varies across patient groups and document types. A system that handles typed English well may perform differently with translated material, dictated notes, or difficult scans. Investigate the failure rather than blaming the patient.

Be clear when automated messages are used. Provide a practical route to a person, especially for disputes, financial hardship, or clinically concerning information.

And avoid incentives that encourage more billable activity without clear clinical purpose.

The most efficient billing system can still be a poor healthcare system if it makes patients carry the friction.

Ethical design asks whether the work disappeared, moved, or became invisible. Those are different outcomes.

Practical considerations: the small practice advantage

Small clinics have fewer resources. They may also have shorter communication lines and a clearer view of recurring problems.

Use that advantage.

Bring a physician, a front desk representative, and a billing specialist together for 30 minutes. Review a handful of recent failures. Ask where information was first available, where it changed, and where someone first noticed the problem.

Do not begin with blame. Begin with sequence.

Then choose a correction that fits ordinary staffing. A solution that requires the practice manager to inspect every record forever is a temporary workaround with a managerial title.

Prefer small, observable changes: a required authorization check for one procedure group, a payer routing rule for one recurring mismatch, or an extraction tool that highlights uncertain fields before entry.

Budget for training and protected review time. Staff cannot simultaneously maintain full production and conduct a careful pilot without a tradeoff somewhere.

Document the fallback before go-live. Who handles incomplete records? Who can pause the tool? Which tasks continue manually? Where are pending items tracked?

A small practice does not need to imitate a hospital's governance department. It needs clear responsibility, useful records, and a workable recovery plan.

Sophistication is knowing which safeguards matter. Complexity is buying all of them without knowing why.

Step by step: a bounded pilot physicians can actually manage

Step one: choose one recurring failure

Select a problem with enough volume to observe and a clear owner. Examples include authorization mismatches, wrong payer routing, or missing information in referral documents. Define the scope narrowly enough that staff can explain it without a slide deck.

Step two: establish a baseline

Review a recent, representative period. Record volume, error frequency, staff time, and downstream consequences. Use enough cases to understand normal variation. A small sample can guide exploration, but it cannot support sweeping performance claims.

Step three: map the earliest useful intervention

Identify where the relevant facts first exist. If the mismatch can be caught during scheduling, do not wait until claim submission. Earlier is useful only when the information is available and the check will not unnecessarily delay care.

Step four: separate rules from interpretation

Use a deterministic check for clear requirements, such as missing fields or a mismatched identifier. Consider AI when the task involves interpreting variable documents. Route uncertainty to someone with the authority and context to resolve it.

Step five: test in observation mode

Compare suggestions with actual staff decisions before allowing consequential actions. Include incorrect dates, duplicate documents, ambiguous payer names, and incomplete scans. Record false alarms and missed problems. Reviewers should see the supporting source, not just a confident answer.

Step six: agree on targets and stop conditions

Set local targets for error reduction, rework, review burden, cost, and safety. For example, a practice might require lower correction time without increased missed authorizations. These are practice-specific pilot criteria, not universal industry standards.

Step seven: run a controlled live pilot

Train the team, limit permissions, and monitor exceptions. Use a defined patient or service cohort where appropriate. Document concurrent changes in payer mix, staffing, volume, or policy so they do not masquerade as software benefits.

Step eight: decide and preserve the learning

Choose to expand, revise, pause, or stop. Allow sufficient follow-up for adjudication and collections before judging financial outcomes. Keep successful rules and failure examples even if the vendor relationship ends. The practice should retain its learning.

Tools, metrics, and resources: a useful minimum

You can begin with existing practice management reports, remittance information, clearinghouse responses, a secure issue log, and a basic time study.

The tools should answer practical questions:

What failed, and at which stage?
What source information supports the correction?
How often does the same cause recur?
How much review does the intervention create?
What changed for staff, patients, and cash flow?

Use payer manuals and authoritative coding resources for requirements. Use CMS materials for relevant Medicare policies. Use qualified coding, compliance, legal, and security support when the question exceeds the team's expertise.

For an AI tool, request a concise system description covering intended use, limitations, information access, evaluation methods, update process, and human oversight. A polished model card can help, but it does not replace local testing.

For staff, provide a short operating guide with screenshots or examples, escalation contacts, and pause instructions. Make it easy to report a wrong answer without starting an argument about the future of technology.

For metrics, review both false positives and false negatives. A system that flags every claim may find every problem while saving nobody any time.

Also track overrides. Frequent overrides can indicate poor rules, unclear instructions, or useful staff knowledge that has not been incorporated.

The best operational tool makes the next decision clearer.

Insights: three reframes that change the buying conversation

First, billing is an information quality problem with financial consequences. This reframing makes scheduling, intake, clinical documentation, and payer rules part of the solution. It also explains why a downstream tool may struggle with an upstream omission.

Second, independence requires operational control. A practice can outsource useful work while retaining access, oversight, and an exit path. Conversely, owning a server does not guarantee control if nobody understands the workflow running on it.

Third, staff knowledge is infrastructure. The billing specialist who recognizes a recurring payer mismatch holds practical knowledge that deserves documentation, review, and preservation.

Ask that person to explain the signal. Convert the explanation into a maintained rule where possible. Keep the source and effective date. Invite correction when circumstances change.

This is especially relevant when modernizing legacy software. Old screens can contain outdated habits. They can also encode decades of useful exceptions. Replacing the interface before understanding the rules risks discarding the part that actually works.

The right question is not whether the old system looks modern. It is which knowledge deserves to survive.

A clinic's advantage may live in the workaround everyone forgot to document.

Modernization should make that knowledge visible before making it portable.

What failure should look like in an honest article

I cannot offer a dramatic OnnX case study here without verified pilot evidence. Inventing one would make the article more exciting and the advice less trustworthy.

Instead, consider a clearly hypothetical failure.

A clinic tests document extraction. The tool reads most fields correctly. Everyone is encouraged. Then the team discovers that reviewers spend longer checking the outputs than they previously spent entering the fields.

The technical demonstration succeeded. The operational pilot failed.

That is useful information.

The next move might be to narrow the document types, highlight only uncertain fields, improve source quality, or stop the project. The wrong move is to redefine success as adoption because the contract has already been signed.

Another hypothetical: an appeals assistant produces persuasive letters, but the practice never fixes the scheduling error generating those denials. Appeal throughput improves. The underlying defect continues.

Here, the failure is choosing the wrong intervention point.

For OnnX, this reinforces the standard I want to apply: prove a bounded improvement before claiming a broad transformation. That is a development principle, not a statement that every capability discussed here is already available.

Trust grows when a founder can distinguish a product goal from a measured result.

A failed pilot can save money. An unmeasured success can spend it indefinitely.

Questions worth asking in the next vendor meeting

Ask the vendor to show a failure before showing another success.

Which documents cause trouble? Which payer changes require manual updates? When does the system refuse to act? What does a reviewer see when the recommendation is uncertain?

Then ask for a complete journey through one ordinary claim. Watch the handoffs. Count the systems. Notice where someone has to copy information or infer what happened elsewhere.

Ask who maintains the connection when an interface changes. Ask what support includes. Ask how long implementation takes for a clinic with your staffing and your software, rather than an ideal customer with an unusually patient IT team.

For interoperability, ask for tested mappings and exception handling. HL7 is a family of standards, and FHIR is itself an HL7 standard. Calling them inherently incompatible languages oversimplifies the problem. Reliable exchange also depends on interfaces, terminology, identity matching, permissions, and local configuration.

AI extraction may help with unstructured material. It cannot make those dependencies disappear by calling itself a universal translator.

Finally, ask: If this does not work, what exactly do we keep?

The answer should include your records and an understandable account of what the system did. Depending on the agreement, it may also include configurations, rules, and transition support.

The exit conversation is part of the purchase conversation.

The Monday morning test: can your team use this?

Before approving a pilot, imagine an ordinary Monday with two absent employees, a full schedule, and an insurer changing its portal password requirements at the least poetic moment.

Can the team still explain the process?

If the answer depends on one unusually knowledgeable employee being available, you have identified a dependency. Document it before adding automation around it.

Try a simple claim review huddle. Select five recent problem claims. Remove unnecessary patient identifiers from the discussion materials. For each claim, identify the first incorrect or missing fact, the person who could have seen it, and the action that would have helped.

Keep payer errors separate from practice errors. Otherwise, staff may be held responsible for decisions outside their control, and the improvement plan will chase the wrong cause.

Then ask what evidence would change your mind about the proposed solution.

Perhaps the missing information was unavailable until after the service. Perhaps the authorization was correct but the payer processed it incorrectly. Perhaps the field was accurate in one system and altered during transfer.

Each possibility points toward a different intervention. One calls for a later check. Another calls for escalation. Another calls for interface repair.

Diagnosis before automation is a useful rule for administrative medicine too.

Assign one person to maintain the issue log and another to review the clinical or coding implications when needed. Agree on a short weekly review. Keep the meeting focused on recurring causes, unresolved exceptions, and actions with owners.

Avoid making the log a museum of old frustrations. Every active issue needs a next step or a reason to close it.

For patient-facing changes, test the wording with someone outside the billing team. Ask them to explain the message back. If they cannot tell whether they owe money, need to provide information, or should simply wait, revise it.

Confusion is work. It usually returns as a phone call.

Finally, run a short downtime exercise. Revoke a test account, simulate an unavailable connection, and confirm that pending tasks remain visible. Do this safely in a test environment or a carefully controlled exercise, without interrupting patient care.

Record who can authorize a pause, contact support, and restore service. Confirm that the fallback preserves privacy and does not lose charges or clinical messages.

These exercises will not look impressive in a launch announcement. They will look very impressive to the employee trying to keep Tuesday from becoming a backlog.

Readiness is the ability to recover on an ordinary day, with ordinary people, using instructions they can actually find.

Before expanding, ask staff whether they would choose to keep the tool if the decision were theirs. Their answer is not the only measure, but it can reveal hidden review burdens that collection reports and vendor satisfaction surveys routinely miss.

Future outlook: the scarce resource will be trustworthy judgment

AI capabilities will continue to change. Prices, products, and regulations will change with them. Predictions about a particular model's dominance are less useful to a clinic than habits that survive those changes.

Three habits seem durable.

Maintain clear source information. Know which document, policy, or clinical observation supports a claim.

Maintain accountable decisions. Know who can approve a recommendation, resolve a conflict, and stop an unsafe process.

Maintain reversible operations. Know how to change a rule, recover a record, and continue when a vendor becomes unavailable.

More capable agents may eventually perform longer administrative sequences. That makes permission design more consequential. Reading a document, editing a record, submitting a claim, and sending a patient message should not automatically share the same authority.

The winning practice will not necessarily own the most AI. It will understand which decisions can be delegated, which require review, and which should remain firmly human.

For independent clinics, this is an opportunity to build stronger operations without surrendering professional identity. But technology should earn responsibility through evidence.

Trust should expand with demonstrated reliability, not with the length of a product roadmap.

Frequently asked questions

Will AI replace our billing staff?

Some tasks may become easier to automate. Others will require more interpretation, oversight, and patient communication. Plan around actual workload and performance. Do not promise staffing reductions before measuring review time, exceptions, and service quality.

Where should a small clinic start?

Choose a frequent, bounded problem with a clear owner and measurable consequences. Missing authorization information or recurring payer routing errors may be useful candidates. The right starting point depends on your practice's actual failures, not a generic vendor checklist.

Does using a shared model train it on our information?

Not inherently. Inference, retention, retrieval, and training are separate processes. Review the vendor's terms, technical arrangements, configuration, and subcontractors. A privacy assurance should identify the specific uses that are prohibited and the controls supporting that promise.

Can patient-generated information create billable services?

It can support care and may contribute to qualifying services when applicable requirements are satisfied. Patient activity alone does not establish reimbursement. Verify the service, payer, medical necessity, documentation, consent, staff involvement, and other relevant conditions before billing.

Is private hosting always the safest choice?

No. Private hosting changes responsibilities and may offer useful control, but safety depends on implementation and maintenance. Compare access controls, monitoring, patching, recovery, contracts, and staffing. Choose an arrangement the practice can govern competently.

How soon should a pilot show financial results?

Operational signals may appear before collections mature. Match the evaluation period to the outcome. Review time and detected errors can be assessed early; adjudication, payment, and aging require longer follow-up. Avoid drawing a cash-flow conclusion from an unfinished claim cohort.

What evidence should we request from a vendor?

Request a clear intended use, representative evaluation results, failure examples, implementation requirements, security information, and contract terms. If customer references are offered, ask whether their setting resembles yours. Evidence from a different specialty or workflow may not transfer reliably.

Should we eliminate every billing intermediary?

Remove unnecessary handoffs, duplication, and unclear accountability. Retain useful expertise and infrastructure when they improve outcomes. The goal is a simpler, more accountable operation. A specialist who prevents errors may add more value than a supposedly direct workflow that creates them.

Final thoughts: make the second attempt unnecessary

Independent physicians do not need another reason to work harder after the patient leaves.

They need accurate information, sensible rules, capable staff, and technology that reduces avoidable friction.

Denial prevention does not mean every denial is preventable. Payer errors, coverage disputes, changing policies, and clinical complexity remain. It means refusing to normalize the errors a practice can reasonably catch earlier.

That is the direction I want OnnX to help advance: fewer unnecessary handoffs, clearer accountability, and practical evidence of improvement for small and medium-sized clinics.

The best result may be surprisingly undramatic. A correct payer. A confirmed authorization. A documented service. A claim that does not return for the same avoidable reason.

No victory music. No heroic appeal. Just an afternoon returned to the practice.

The most valuable administrative work may be the work you no longer need to do.

Get involved: change the conversation

If your clinic became twice as fast at billing tomorrow, would it solve the problem—or merely process the same mistakes twice as quickly?

Comment with the one recurring billing failure you would eliminate first, without sharing patient information, and help other physicians learn from your experience.

Repost this article so another physician or clinic owner can question an expensive habit before buying a faster version of it.

References

Calendar Year 2027 Medicare Physician Fee Schedule Final Rule
CMS’s November 4, 2026 fact sheet explains finalized physician payment policies. A useful starting point for reviewing affected services, effective dates, and billing workflow updates.
CMS Finalizes CY 2027 Physician Fee Schedule to Shift from Sick Care to Healthcare and Curb Waste
This November 4, 2026 HHS announcement presents the administration’s policy rationale and includes Dr. Mehmet Oz’s statement quoted beneath this article’s title.
Calendar Year 2027 Medicare Physician Fee Schedule Final Rule—Medicare Shared Savings Program
CMS’s November 4, 2026 summary outlines changes relevant to accountable care participation, financial incentives, and practice planning.

Your Medical Billing Problem May Not Be a Billing Problem: The Hidden Cost of Creating Work You Should Never Have to Do
Explores how recurring billing failures begin upstream—and how tracing their causes can help practices reduce corrections, delays, and unnecessary administrative work.
We Trained Doctors to Save Lives. Then We Made Them Professional Clickers.
Examines the connection between physician administrative burden, fragmented workflows, and technology that creates activity without necessarily improving care or operations.
Healthcare Has a Strange Business Model: We Pay People to Fix Mistakes We Pay Software to Create
Challenges the assumption that faster automation always means better performance, emphasizing information quality, reliable handoffs, and prevention of avoidable billing failures.

About the author

Dr. Daniel Cham is a physician, medical consultant, and entrepreneur working at the intersection of medical technology, healthcare management, and medical billing. As founder of OnnX, he focuses on practical ways to reduce administrative burden and strengthen independent clinical practices. He writes about the operational decisions connecting patient care, information quality, and innovation. Connect with Dr. Cham on LinkedIn.

A note on scope

This article offers general education and professional commentary. It does not provide individual medical, legal, billing, or compliance advice. Requirements vary by payer, service, jurisdiction, and effective date. Consult appropriately qualified professionals before applying these ideas to specific decisions. Illustrative scenarios and calculations are identified as hypothetical and do not represent verified customer results.

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