“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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