Daniel Cham MD

Tuesday, October 20, 2026

Katherine Screws Waited Three Years for a Transplant. What If Healthcare Is Solving the Wrong Problem?

A patient’s search for answers reveals an uncomfortable truth about complex systems: when information is fragmented, people can follow the process and still get stuck. From transplant care to medical billing, are we fixing problems—or simply getting better at cleaning up the mess?

“Whatever the organ, most patients don’t know what to ask,” said Dr. Allyson Hart, a researcher at Hennepin Healthcare Research Institute, in Associated Press

It is a simple observation with a much bigger implication.

What happens when people must navigate a complicated system without understanding how it works, which options are available, or what is standing in their way?

Sometimes, the problem is not a lack of effort.

Sometimes, it is not a lack of expertise.

And sometimes, it is not even a lack of technology.

Sometimes, people simply cannot see the information they need to make their next move.

That is the question raised by the experience of Katherine Screws, a Phoenix woman whose three-year wait for an intestine transplant illustrates the human consequences of navigating a complicated healthcare system.

Her story also raises a question for physician-owned practices: How much administrative frustration could be avoided if the right information became visible before the next step went wrong?

Because healthcare has an impressive collection of software, portals, databases, dashboards, and artificial intelligence.

Yet somehow, people still end up asking the digital equivalent of, “Has anyone seen the paperwork?”

Apparently, the paperwork has achieved interoperability with everyone except the person who needs it.

A six-month expectation became a three-year wait

In May 2022, one month after marrying her husband, Stephen Gerrish, Katherine Screws was involved in a car crash that destroyed her small intestine.

She could no longer absorb nutrition normally. She depended on overnight intravenous nutrition, often felt too sick to get out of bed, and could not return to her engineering career or pursue her plans to start a family.

She had been told that a transplant might be possible within approximately six months.

Instead, she waited three years.

During that time, Screws faced more than a medical challenge. She was trying to understand a system whose options, processes, and decisions were not always clear to her.

The turning point came when she connected with Lisa Stocks, a nurse at California's organ procurement organization, OneLegacy.

Stocks helped Screws understand that transplant centers can differ in their practices and in which donor organs they are willing and able to accept. Screws also learned about publicly available information that could help her compare transplant centers.

She explored the Scientific Registry of Transplant Recipients database and registered at MedStar Georgetown University Hospital in Washington, D.C., in addition to her original center, UCLA Health.

In June 2025, just weeks after registering at Georgetown, she received her intestine transplant.

Dr. Cal Matsumoto, a transplant surgeon at MedStar Georgetown University Hospital, and his team performed the procedure. The donor intestine was longer than usual, but it fit successfully.

The story also features Dr. Syed Ali Husain of NYU Langone Health, who researches differences in transplant practices, and Dr. Allyson Hart, who helped develop patient-friendly tools for comparing transplant centers.

UCLA Health declined to comment on Screws's individual care, citing patient privacy rules, but said it follows federal transplant guidelines and continually strives to improve its processes.

Screws was careful to explain that her frustration was not simply directed at her original physicians. She was troubled by the lack of clear, centralized information about what to expect and what might be holding up progress.

Her experience does not prove that every transplant delay is avoidable or that one hospital's decisions are inherently better than another's. Transplantation involves difficult clinical judgments, complex logistics, and time-sensitive matching.

But it does demonstrate the importance of understanding how a system works and knowing what questions to ask.

The most important question may not be, “Why is this taking so long?” It may be, “What is actually holding this up—and what can I do about it?”

That question belongs in many more places across healthcare.

The contrarian argument: Maybe healthcare doesn't need another dashboard

When something goes wrong, the instinct is often to add something.

Another application. Another workflow. Another reporting tool. Another automated notification. Another dashboard showing that the other dashboard has a problem.

Before long, the organization has a dashboard for the dashboard.

But what if the underlying problem is not always the absence of a tool?

What if the information already exists somewhere—but is incomplete, disconnected, outdated, difficult to interpret, or unavailable to the person who needs it?

Consider how a medical practice operates.

The physician understands the patient's clinical circumstances. The front office manages registration. The authorization team works with payer requirements. The scheduler coordinates appointments. The medical biller needs the documentation and information required to submit an accurate claim.

Each person may be doing their job correctly.

Yet the overall process can still fail if important information does not reach the next person at the right time.

A patient's coverage may have changed. A referral may be missing. An authorization may still be pending. A documentation requirement may not have been identified until after the encounter.

The practice discovers the problem later, when correcting it is more expensive and disruptive.

This is the distinction between optimizing a task and improving the system that connects the tasks.

A faster process can still be a bad process. An automated error is still an error. And an AI-generated answer cannot make missing information magically appear.

Technology can improve healthcare enormously. But adding technology without addressing the quality and continuity of information may simply help an organization process its existing problems more efficiently.

That is progress of a sort.

It is just not necessarily the progress we need most.

What Katherine's experience can teach us—and what it cannot

An intestine transplant and a rejected medical claim are not equivalent.

The medical stakes, ethical considerations, clinical decisions, and consequences are fundamentally different. Katherine's story should not be used to suggest that a billing platform could have shortened her transplant wait, nor should it be interpreted as an accusation of negligence against the professionals involved.

The connection is more specific.

People need understandable, timely information to navigate complex systems and make informed decisions.

For a transplant patient, that may mean understanding how centers differ, which options are available, and what questions to discuss with a transplant team.

For a physician-owned practice, it may mean knowing whether a referral is complete, whether authorization is confirmed, whether coverage information is current, and whether documentation supports the service being billed.

Different situations. Different stakes. A shared challenge: information must be available and understandable before it can guide action.

In both settings, the existence of a process does not guarantee that the people navigating it can see how it works.

That is why information transparency matters.

The hidden cost of discovering problems too late

Imagine a physician evaluating a patient and determining that a procedure is medically appropriate.

The appointment is scheduled. The clinical note is written. The patient expects the next step to happen.

But several administrative questions remain:

Is the insurance information current?
Does the patient's plan cover the service under the applicable conditions?
Is prior authorization required?
Is a referral necessary?
Does the documentation contain the information required to support the request?
Has the authorization status reached the scheduling team?
Will the eventual claim accurately reflect the service provided and documented?

These questions may involve different staff members, systems, and payer requirements.

If one requirement is missed, the problem might not become visible until the procedure is scheduled, the service is performed, or the claim is submitted.

Then the practice must investigate.

Someone retrieves the documentation. Someone contacts the payer. Someone follows up on the authorization. Someone corrects the claim. Someone explains the delay to the patient. Someone tries to determine why the process failed.

And the physician may be asked to clarify a note weeks after the encounter.

The organization has now paid for the work twice: once to perform the original task and again to repair the process.

This is where administrative friction becomes a financial and operational problem.

A denied claim is not just a line in a report. It can represent staff time, delayed reimbursement, additional patient communication, and work that could have been directed elsewhere.

The critical question is not merely, “How do we fix this claim?”

It is:

“What happened before the claim that made this problem possible?”

That is the upstream question.

The prior authorization problem is not just paperwork

The American Medical Association's prior authorization research has documented substantial administrative burdens for physicians and patients.

Its 2025 physician survey, reported in 2026, found that:

95% of surveyed physicians said prior authorization delays access to necessary care.
92% said it negatively affects clinical outcomes.
79% said patients abandon treatment because of authorization challenges.
94% said prior authorization contributes to physician burnout.
Physicians and staff reported spending an average of 13 hours per week dealing with prior authorization.

These are survey findings, not proof that every delay causes harm or that one technology solution can eliminate the burden. But they illustrate how much effort the process can consume.

For a small practice, 13 hours a week is substantial.

That time has to come from somewhere: patient communication, referral coordination, follow-up, documentation, or other work that also matters.

And the irony is hard to miss. Healthcare continues to invest in digital transformation while physicians and staff spend hours navigating administrative requirements across fragmented systems.

We have digitized many processes.

We have not necessarily connected them.

Three lessons from the people in Katherine's story

1. Katherine Screws: People cannot act on options they cannot see

Screws's experience shows why accessible information matters.

For practices, the equivalent question is whether each authorized team member can see the information needed to move work forward.

Can the scheduler see that authorization is pending?

Can the physician understand which documentation requirement remains unresolved?

Can the biller distinguish a genuine coding question from an eligibility issue?

Can the practice owner identify recurring problems without manually reconstructing events from multiple systems?

The answer is not to give everyone unrestricted access to everything. Privacy, security, and role-based access are essential.

The objective is to make relevant information available to the right person at the right time.

2. Lisa Stocks: Experience is valuable—but it should not live only in someone's memory

Stocks helped Screws understand the transplant system and explore additional options.

Medical practices also depend on people with experience. A skilled biller may know that a particular payer repeatedly requests certain documentation. A referral coordinator may understand which records a specialist requires. A front-office employee may recognize a recurring eligibility problem.

That knowledge is valuable.

But if it exists only in someone's memory, a personal spreadsheet, or an old email thread, the organization cannot reliably apply it across future encounters.

The solution is not to replace experienced employees.

It is to preserve useful knowledge, make recurring requirements visible, and reduce dependence on memory alone.

Even an outstanding employee cannot simultaneously be the practice's database, notification system, and institutional memory.

3. Dr. Syed Ali Husain, Dr. Allyson Hart, and Dr. Cal Matsumoto: Context changes how information should be interpreted

The transplant reporting describes differences among centers and the clinical judgment involved in evaluating donor organs.

That reinforces a broader principle: A decision cannot be understood simply by looking at its outcome without considering the circumstances behind it.

The same is true of medical billing.

A denial does not automatically mean a coder made a mistake. A missing authorization does not necessarily mean a scheduler was careless. A documentation gap does not automatically mean a physician failed to provide appropriate care.

The cause could involve changing payer requirements, incomplete registration, a communication breakdown, a documentation issue, or a process that failed to make an important requirement visible.

Counting failures tells us what happened. Understanding their causes tells us what to change.

That is the difference between reporting a problem and learning from it.

The myth: More AI automatically means less administrative work

AI can help identify patterns, summarize information, flag potential inconsistencies, and support routine tasks.

But AI is not a substitute for good data, appropriate clinical judgment, or a well-designed process.

Imagine an automated system submitting an authorization request with incomplete information. It can submit the request quickly, but the payer may still return it for missing details.

Imagine an AI tool generating a polished clinical note that omits a fact necessary to explain the service. The language may sound excellent while the underlying problem remains.

Imagine a denial-management platform that helps staff categorize rejected claims. That may be valuable, but categorizing the same recurring problem does not necessarily prevent it from happening again.

None of these tools is inherently bad. Each may address part of the problem.

The mistake is assuming that automating one step automatically improves the entire process.

In a September 30, 2026, joint statement, six major physician organizations—including the American Medical Association, the American College of Physicians, and the American College of Surgeons—warned against treating AI as a replacement for physician expertise. They emphasized that clinical context, professional judgment, patient safety, and accountability must guide the development and use of AI.

The lesson for healthcare administration is equally important: Technology should support better decisions, not simply accelerate activity.

Before adopting an AI tool, ask:

Does it help identify the underlying issue?
Does it use reliable, relevant information?
Does it explain what remains unresolved?
Does it help the appropriate person take the next step?
Can the practice audit its outputs and correct mistakes?
Does it preserve human oversight where judgment is required?

If the tool only makes the existing queue move faster, it may still be useful. But it is solving a different problem from preventing avoidable work.

What upstream billing intelligence looks like in practice

Upstream billing intelligence starts with the idea that many preventable billing problems have contributing conditions that exist before a claim is submitted.

Those conditions can arise during registration, eligibility verification, scheduling, referral management, authorization, documentation, coding, or charge capture.

A practical framework is:

Capture → Structure → Preserve → Propagate → Act → Learn

Capture: Collect relevant information when it becomes available.

Structure: Organize it so people and systems can interpret it consistently.

Preserve: Retain the context behind important decisions, requirements, and exceptions.

Propagate: Make the information available to authorized people and downstream systems that need it.

Act: Flag unresolved requirements and help the responsible person determine the next appropriate step.

Learn: Analyze recurring patterns and improve the process based on evidence.

For example, if a procedure requires authorization, the practice should be able to distinguish an authorization that is requested from one that is approved or confirmed as unnecessary.

If documentation appears to be missing a required element, the system should flag the issue for appropriate review rather than silently assuming the record is complete.

If a payer repeatedly requests the same information, the practice should investigate whether a template, training, handoff, or process needs improvement.

This does not mean every denial can be prevented. Some result from payer decisions, clinical disagreements, changing rules, or circumstances outside the practice's control.

It means the practice should identify and address the preventable conditions it can influence.

The best time to discover a missing requirement is generally before additional work depends on it.

Seven metrics that reveal whether the process is improving

A practice does not need 47 dashboard widgets to start making progress. It needs a few consistent measures that lead to useful decisions.

First-pass claim acceptance: How often do claims pass initial submission checks without preventable errors? Distinguish clearinghouse acceptance from payer adjudication.
Denial rate by root cause: Separate authorization, eligibility, coding, documentation, and other relevant causes rather than relying only on the overall rate.
Rework hours: Measure staff time spent correcting preventable errors, retrieving information, and resubmitting claims.
Authorization lead time: Track the time required to move from a complete request to a documented decision, separating payer delays from internal delays.
Missing-information rate: Measure how often work reaches a critical step without the information required to proceed.
Repeat-error rate: Determine how frequently the same underlying problem recurs after it has been addressed.
Patient-impact indicators: Where appropriate, monitor avoidable rescheduling, care delays, unexpected financial communications, and related outcomes.

Establish a baseline, define each measure consistently, and review the results regularly.

The goal is not to make the dashboard prettier.

It is to answer a more important question:

Are we preventing more problems—or merely getting better at cleaning them up?

A four-week experiment for a small medical practice

Improving a process does not necessarily require purchasing a new platform.

Start with one recurring problem.

Week 1: Find the pattern.

Review a sample of recent denials, authorization delays, or claim corrections. Identify where the issue first appeared.

Week 2: Map the information.

Determine where the required information originates, who verifies it, where it is stored, who needs it next, and how the current process fails.

Week 3: Test one change.

Try a targeted checklist, a standardized field, a clearer handoff, or an appropriate alert. Avoid adding steps that do not address a defined problem.

Week 4: Measure the result.

Compare the results with the baseline. Review error frequency, rework hours, delays, and staff feedback. Check for unintended consequences.

If the change helps, refine it. If it does not, investigate why before automating it.

This is not the most glamorous AI demonstration. Nobody is going to make a viral video about a missing authorization field that was completed correctly the first time.

But that is precisely the point.

A problem prevented is often less visible than a problem repaired.

It is also frequently less expensive to deal with.

Where OnnX 2.0 fits

This is the problem OnnX 2.0 is being developed to address.

OnnX is an upstream intelligence platform for healthcare, built by physicians and medical billers to reduce downstream administrative and billing noise so physicians can focus on clinical care.

Built by physicians and medical billers since 1995, the OnnX 2.0 concept focuses on the information and decisions that influence billing before a claim becomes a problem.

Clinical context, patient information, coverage, authorization requirements, referrals, documentation, and operational decisions can become fragmented across people and systems.

When the relevant information does not travel with the work, downstream teams must reconstruct it.

OnnX 2.0 is being developed around a different approach: make important context more structured, visible, and actionable so physician-owned practices can recognize information gaps earlier and investigate risks before they become avoidable rework.

The objective is not to promise zero denials or claim that every billing problem can be eliminated. It is to focus on upstream causes that a practice may be able to identify and address.

That means asking different questions.

Instead of only asking how to process a denial faster, ask what created the conditions for it.

Instead of only asking how to chase missing information, ask why it was missing when it was needed.

Instead of buying another dashboard, ask which decision the dashboard should help someone make.

The aim is to move from reactive billing toward more predictable, informed revenue-cycle operations.

That is a development vision, not a claim that every capability has already been validated in practice. The value must ultimately be demonstrated through real workflows, measurable results, and appropriate safeguards.

But the principle is worth pursuing: Better information upstream can create better opportunities to prevent problems downstream.

The future of healthcare may depend on what we stop having to fix

Katherine Screws's experience reminds us that navigating a complicated system can be difficult even when the people inside it are working hard.

Her story does not prove that every delay is avoidable. It does show why accessible information, clear explanations, and an understanding of available options matter.

For physician-owned practices, the parallel is not the severity of the outcome. It is the importance of visibility and informed action.

A patient should not have to guess what question to ask next.

A physician should not repeatedly reconstruct the context behind a clinical decision.

A medical biller should not have to become a detective because essential information is scattered across disconnected systems.

And a practice owner should not have to wait until the end of the month to discover a recurring problem that could have been visible much earlier.

Healthcare does not need technology for technology's sake.

It needs systems that help people do the right work with the right information at the right time.

We have spent years teaching technology to process more work.

Perhaps the next breakthrough will come from designing systems that recognize which problems should never have reached the next step in the first place.

And if that sounds less exciting than another AI announcement, consider this:

Sometimes, the best result is a claim that never needed correcting, an authorization that did not stall, or a patient who did not have to make five phone calls to discover what happens next.

No dramatic alert. No heroic spreadsheet. No billing department operating like a crime-scene investigation unit.

Just a process that works better.

That may not be flashy. But for physicians, staff, and patients, it could be far more valuable.

Frequently asked questions

What is upstream billing intelligence?

It is an approach to identifying and addressing information gaps, requirements, and operational risks before they contribute to claim errors, denials, or rework.

Why do billing problems begin before a claim is submitted?

Claims depend on information collected across registration, eligibility, referrals, authorization, documentation, coding, and charge capture. Errors or omissions at earlier stages can affect the claim later.

Can AI prevent medical billing denials?

AI may help identify patterns, missing information, and potential inconsistencies. It cannot guarantee payment or eliminate every denial. Results depend on data quality, payer rules, workflow integration, and appropriate human oversight.

What should a small practice do first?

Choose one recurring problem, identify where it originates, map the information and handoffs involved, test one targeted improvement, and measure whether the change reduces errors or rework.

Is OnnX 2.0 a traditional medical billing service?

OnnX 2.0 is being developed as an upstream intelligence platform focused on helping physician-owned practices improve the information and decisions that affect billing. Its intended positioning emphasizes prevention and intelligence rather than outsourced billing services.

Join the conversation

Katherine Screws's story raises a question that reaches far beyond transplant medicine:

Are we building smarter healthcare systems—or simply getting better at managing the problems our systems keep creating?

Physicians, practice owners, administrators, and medical billers: Where does the most avoidable rework begin in your experience—registration, eligibility, authorization, documentation, coding, or the handoffs between them?

Share your perspective in the comments. If this challenges how we think about healthcare administration, share or repost it so others can join the discussion.

About the author

Daniel Cham, MD is a physician, entrepreneur, and healthcare technology founder developing OnnX 2.0, an upstream intelligence platform for healthcare built by physicians and medical billers since 1995. His work focuses on improving the quality and continuity of information across clinical and administrative processes so physician-owned practices can reduce preventable billing problems and focus more attention on patient care.

Disclaimer: This article is for educational and informational purposes only and does not provide medical, legal, coding, billing, or reimbursement advice. Katherine Screws's experience is discussed based on public reporting and should not be interpreted as an allegation of wrongdoing by any individual or organization. The comparison with medical billing concerns information and coordination, not equivalence between clinical outcomes and administrative delays.

References

Associated Press, October 20, 2026: Arizona woman's three-year wait for an intestine shows the US organ transplant system's complexity.
Associated Press, October 20, 2026: How the US organ transplant system works and what to ask to improve your chances.
American Medical Association: Survey: Prior authorization reform pledge falls short for physicians.
American Medical Association, September 30, 2026: Statement from leading physician organizations on the role of augmented intelligence in healthcare.
Agency for Healthcare Research and Quality: Care Coordination.

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#Healthcare #PhysicianOwnedPractices #MedicalBilling #HealthcareAdministration #RevenueCycleManagement #PriorAuthorization #ClaimDenials #HealthcareAI #HealthTech #OnnX2.0 #UpstreamIntelligence #PhysicianBurnout

Monday, October 19, 2026

Polly Comforted a Child With Cancer. Meanwhile, Healthcare Is Still Paying People to Fix Preventable Problems.

What a Labrador at a Bronx children's hospital can teach physicians, clinic owners, and healthcare innovators about patient experience, administrative waste, and the contrarian idea that healthcare's next breakthrough may come from preventing work, not automating more of it.

“First, experience – and patient experience in particular – is not a survey or score. Rather, it is something a patient, family members or care partners live through.”

That is Jason A. Wolf, president and CEO of The Beryl Institute, in Healthcare IT News on October 19, 2026. His point is simple: healthcare is not just what organizations measure. It is what people actually experience.

And sometimes, that experience is shaped by a four-legged healthcare worker who has never attended medical school, never opened an electronic health record, and probably has no opinion about artificial intelligence.

Her name is Polly.

A Dog Named Polly, a Child With Cancer, and an Uncomfortable Healthcare Question

At Children's Hospital at Montefiore Einstein in the Bronx, New York, a four-year-old Labrador named Polly helps bring comfort to hospitalized children.

According to a New York Post report published October 19, 2026, Polly visited an eight-year-old cancer patient who was experiencing tremors after doctors removed the child's breathing tube and reduced sedative medications.

Polly works with Katy Dessick, a certified child life specialist. Dessick reportedly observed that the child's tremors became less frequent when Polly was present.

It is an affecting story, but accuracy matters. This observation does not establish that Polly caused the change in tremors, and it does not mean a facility dog can replace medical treatment. It reminds us that the experience of being ill includes more than clinical procedures.

The report also describes Dr. Adit L. Tal, a pediatric hematologist-oncologist at Children's Hospital at Montefiore Einstein, discussing the role facility dogs can play in helping young patients feel more comfortable.

Another Labrador, Lev, age seven, visits children at Maimonides Medical Center in Brooklyn, New York. His handler is identified in the report by the first name Gabriel.

These are different people doing different jobs in different hospitals. Yet their stories point toward an important principle: the human experience of healthcare deserves attention alongside clinical treatment.

Now, let's move from a children's hospital to a small independent medical practice.

A patient arrives for an appointment. The clinician is ready. The patient is ready.

Then someone discovers the referral is missing.

The authorization status is unclear. The insurance information in one system does not match the information in another. A document that should have been available yesterday now requires three phone calls, two portal logins, and a heroic amount of patience.

Suddenly, the appointment has a supporting cast of administrative problems.

Healthcare has become remarkably good at turning one missing piece of information into a multi-department investigation.

What if the real innovation is not getting better at fixing these problems, but preventing more of them from happening in the first place?

That is the question I believe physicians, clinic owners, and healthcare technology founders should be asking.

My Contrarian Take: Healthcare Does Not Need Another Tool for Every Problem

I am a physician-entrepreneur and founder of OnnX, an AI-powered medical billing software initiative focused on small and medium-sized medical practices.

My perspective is increasingly shaped by one distinction:

Automating work is not the same as eliminating the need for that work.

Suppose a claim is rejected because required information is missing.

One solution is to identify the problem, locate the information, correct the claim, resubmit it, and monitor the outcome.

That may be necessary. But it is not the end of the investigation.

Why was the information missing? Was it never collected? Was it entered inconsistently? Did a handoff fail? Was the requirement misunderstood? Did the information exist but remain inaccessible to the person who needed it?

If the same cause produces the same correction week after week, making the correction faster may improve productivity without solving the underlying problem.

It is a bit like buying a faster mop while ignoring the leaking pipe.

The mop may be excellent. The floor may look better. The water bill remains a mystery.

Healthcare should absolutely improve necessary work. It should also challenge unnecessary work.

Not every denial is preventable. Not every administrative step is waste. Documentation, authorization, compliance, and appropriate review can be essential to safe and accountable care.

The opportunity is to distinguish necessary complexity from preventable rework.

The goal should not be to automate every problem. It should be to make fewer preventable problems occur.

The Administrative Burden Is Not Just an Annoyance

For independent physicians, administrative complexity is a business problem, a workforce problem, and sometimes an obstacle to timely care.

The American Medical Association's 2025 prior authorization survey, reported in May 2026, surveyed 1,000 practicing physicians. The findings are sobering:

95% said prior authorization delays access to necessary care.
79% reported that patients abandon treatment because of authorization challenges.
26% said prior authorization had contributed to a serious adverse event involving a patient in their care.
Physicians reported completing an average of 40 prior authorizations per week.
Prior authorization consumed an average of 13 hours of physician and staff time each week.
94% said prior authorization contributes to physician burnout.
88% reported that prior authorization increases overall healthcare utilization.

These are physicians' reported experiences, not proof that every practice faces the same circumstances or that one software product can resolve them. But the findings make one thing difficult to ignore: administrative processes can consume resources that healthcare teams need elsewhere.

Imagine reclaiming even a portion of the time spent chasing information, checking status, and repeating work. What would a practice do with those hours?

More patient communication? Better follow-up? Less overtime? More time for complex cases?

Those are worthwhile questions. But the answers should be measured, not assumed.

Three Perspectives on the Human Side of Healthcare

1. Jason A. Wolf: Patient Experience Is What People Live Through

In his October 19, 2026, Healthcare IT News interview, Jason A. Wolf argued that patient experience extends beyond surveys and scores. It includes the experiences of patients, families, care partners, and the workforce.

The implication for clinic owners is that patient experience cannot be delegated entirely to a satisfaction survey.

A patient who repeatedly supplies the same information, waits for an unresolved referral, or receives confusing instructions is experiencing the system, too.

So is the staff member who spends half the morning tracking down a document that should already be available.

The practical question is not simply, “How satisfied are our patients?”

It is also, “Where does our process create avoidable friction, and what are we doing about it?”

2. Katy Dessick: Pay Attention to the Person, Not Just the Task

Katy Dessick's work with Polly offers a reminder that care happens in the context of human emotion and vulnerability.

A child facing cancer is not simply a diagnosis, a treatment plan, or a series of clinical measurements. The child's experience matters.

Likewise, an administrative problem is not merely a line item in a work queue. It may affect a patient's next appointment, a staff member's workload, or a clinician's ability to complete a task efficiently.

The lesson is not that every practice needs a therapy dog. It is that healthcare teams should understand the people affected by the processes they design.

3. Researchers: Good Ideas Still Need Evidence

A randomized clinical trial published in JAMA Network Open in March 2025 examined therapy dogs added to standard child-life support in a pediatric emergency department.

The study included 80 children. At 45 minutes, the group that received therapy-dog support experienced a greater reduction in reported anxiety than the comparison group. At 120 minutes, the difference in child-reported anxiety was not statistically significant.

The study supports a measured conclusion: therapy dogs may help reduce anxiety in this setting, but the results should not be exaggerated beyond the evidence.

Healthcare technology deserves the same standard.

A compelling demonstration is not proof of sustained operational improvement. A promising AI feature is not proof of lower costs. A faster workflow is not automatically a better outcome.

Measure the result. Check the limitations. Keep what works. Change what does not.

These individuals and researchers are included for their relevant perspectives; their inclusion does not imply endorsement of OnnX.

OnnX: Move Intelligence Upstream

OnnX is being developed around an idea: many administrative problems should be addressed before they create downstream work.

Medical billing does not begin when a claim is submitted. The eventual claim depends on information gathered, documented, checked, and transferred throughout the patient's journey.

A problem that starts upstream can become a correction downstream.

For example, consider a service that requires prior authorization. A practice may need to identify the requirement, collect supporting information, submit the request, track its status, verify the response, and ensure the result is available to the people who need it.

An upstream approach would focus on making those steps visible and reliable before the service or claim reaches a point where missing information causes avoidable disruption.

That means distinguishing a request that has been submitted from an authorization that has actually been approved. It means flagging gaps without inventing answers. It means routing unresolved issues to an accountable person.

The same principle may apply to referral status, insurance eligibility, documentation completeness, coding checks, and claim preparation.

The OnnX design philosophy follows six steps:

Capture → Structure → Preserve → Propagate → Act → Learn

Capture relevant information at the appropriate point.
Structure it so it can be interpreted consistently.
Preserve context rather than losing it between systems.
Propagate it to authorized people and workflows that need it.
Act through appropriate, clearly assigned next steps.
Learn from outcomes and recurring failures.

The ambition is not to replace every system or automate every decision. It is to explore how upstream intelligence can help practices identify information gaps earlier and reduce avoidable administrative work.

This is a product direction under evaluation, not a claim that every capability is already available or that specific savings have been demonstrated.

A responsible healthcare founder should be able to distinguish a vision from a validated result.

Seven Steps Clinic Owners Can Take Now

You do not need a large AI budget to begin improving a process. Start with a problem that is specific, recurring, and measurable.

Step 1: Identify the recurring headache.

Ask staff which administrative issue interrupts their work most often. Look for missing referrals, unclear authorization status, inconsistent insurance details, repeated documentation requests, or preventable claim corrections.

Step 2: Map what really happens.

Document the actual workflow, including handoffs, systems, exceptions, and follow-up. The official process may have three steps. The staff's spreadsheet, phone calls, and workarounds may reveal ten.

Step 3: Establish a baseline.

Count how often the problem occurs, how long it takes to resolve, and what consequences follow. Record staff time and relevant operational outcomes where practical.

Step 4: Investigate the root cause.

Ask why the problem happened, not just who fixed it. Was information never collected? Was ownership unclear? Did a transfer fail? Was a requirement misunderstood?

Step 5: Standardize the process.

Define what information is needed, where it belongs, who verifies it, and how completion is confirmed. Do not remove safeguards or clinically important exceptions merely to make the workflow look simpler.

Step 6: Pilot a narrow improvement.

Try a structured checklist, clearer ownership, a validated integration, or a carefully scoped automation. Involve the staff who will use it. Keep human review for consequential decisions.

Step 7: Compare results and adjust.

Did the problem happen less often? Did resolution time fall? Did the change create new errors? Did staff find the process easier to use?

If the evidence is weak, do not declare victory because the software has a nice interface.

Six Ways Automation Can Make Things Worse

1. Automating a broken process. A faster bad process is still a bad process.

2. Adding alerts without responsibility. If nobody owns the next action, another notification is just digital confetti.

3. Trusting AI output without verification. AI can miss context, misinterpret data, or produce plausible but incorrect answers.

4. Measuring activity instead of results. The number of tasks automated tells you little about whether the practice improved.

5. Ignoring staff feedback. A tool that looks efficient to management may create extra work for the people using it.

6. Promising savings before measuring them. Claims about reduced denials, recovered revenue, or time saved should be supported by appropriate evidence.

The underlying principle is simple: buy technology to solve a defined problem, not to prove that your organization is keeping up with technology.

Five Myths Worth Challenging

Myth 1: Every denial can be prevented.

Reality: Some denials arise from correctable administrative problems; others involve coverage, clinical documentation, medical necessity, contractual terms, or payer decisions. Diagnose the cause first.

Myth 2: More automation always means lower costs.

Reality: Automation can introduce implementation, oversight, exception handling, and correction costs. Measure the net result.

Myth 3: Billing is only the billing department's responsibility.

Reality: Billing outcomes can depend on information collected during scheduling, registration, clinical documentation, referral management, and authorization.

Myth 4: AI can replace professional judgment.

Reality: AI may assist with information retrieval, prioritization, and consistency checks. It does not eliminate accountability for clinical decisions, coding, or appropriate oversight.

Myth 5: Patient experience and operational efficiency are separate goals.

Reality: Clearer information and fewer avoidable handoffs may support both, but the benefits should be tested rather than presumed.

Legal and Ethical Guardrails

Healthcare automation must respect applicable privacy, security, billing, coding, and contractual requirements.

Before using a new tool, practices should evaluate access to protected health information, vendor responsibilities, security controls, applicable business associate agreements, auditability, and procedures for handling errors or incidents.

An automated system must not invent clinical documentation, misrepresent authorization status, or submit unsupported claims. AI-generated content should be reviewed at a level appropriate to its intended use and potential consequences.

Ethical implementation also requires attention to fairness, transparency, privacy, and accountability. A tool that saves time but introduces hidden errors is not a success.

Consult qualified compliance, legal, coding, and security professionals where appropriate. This article is educational, not legal or billing advice.

Seven Measures That Matter More Than a Flashy Dashboard

Start small. Choose a handful of measures that reflect the problem you are trying to solve:

Rework frequency: How often does the same issue recur?
Resolution time: How long does it take to close the loop?
First-pass claim acceptance: What share of claims are accepted without initial rejection or correction, using a consistent definition?
Denial rate by cause: Which causes are within the practice's control?
Days in accounts receivable: How long does it take to collect outstanding balances?
Administrative time: How much staff time goes toward repetitive work?
Patient impact: Are avoidable delays, repeated requests, or communication failures decreasing?

Interpret changes carefully. Payer mix, claim volume, staffing, and policy changes can affect results. A pilot should be evaluated against a credible baseline, not a convenient story.

What the Future of Healthcare Should Look Like

Healthcare will never be free of complexity. Patients have different needs. Clinical judgment remains essential. Payer requirements and exceptional circumstances will continue to exist.

But complexity is not a reason to accept every source of rework as inevitable.

The next generation of useful healthcare systems may not be defined by how many tasks they automate. It may be defined by how well they preserve information, identify gaps, coordinate handoffs, and support the right action at the right time.

For physicians, that means demanding evidence from vendors.

For clinic owners, it means understanding the problem before buying a solution.

For founders, it means building around real operational needs rather than adding AI because AI is fashionable.

For patients, the hoped-for result is straightforward: less avoidable confusion, fewer preventable delays, and more attention from the people caring for them.

And that brings us back to Polly.

A facility dog cannot repair a broken data handoff. An AI platform cannot replace human compassion. A billing system cannot solve every problem in healthcare.

But each has a role when it serves the people who depend on the system.

Polly's story reminds us to look beyond the procedure and see the person. The administrative burden reminds us to look beyond the rejected claim and investigate the process that produced it.

Perhaps healthcare's next breakthrough is not doing everything faster. Perhaps it is needing to fix fewer things in the first place.

Your Turn: What Work Should Never Have Existed?

If you own or operate a medical practice, consider this question:

What is one recurring administrative task in your practice that should be prevented rather than automated?

Share your experience in the comments. Your answer may help another practice recognize a problem it has accepted as normal.

If this perspective resonates with you, repost the article to start a conversation among physicians, clinic owners, medical billers, and healthcare innovators.

I am exploring these questions through OnnX and welcome thoughtful perspectives from people doing the work every day.

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

References and Further Reading

1. Patient experience and leadership

Jason A. Wolf, president and CEO of The Beryl Institute, discusses why patient experience should be part of organizational culture and strategy, not merely a score.

Healthcare IT News, October 19, 2026: Patient experience is not a score – it's a leadership strategy

2. The administrative burden of prior authorization

The American Medical Association's 2025 physician survey, reported in May 2026, describes physicians' experiences with delays, workload, and prior authorization reform.

American Medical Association, May 13, 2026: Prior authorization reform pledge falls short with physicians

3. Research on therapy dogs and pediatric anxiety

A randomized clinical trial evaluated therapy dogs alongside standard child-life support in a pediatric emergency department.

Kelker et al., JAMA Network Open , March 3, 2025: Therapy Dogs for Anxiety in Children in the Emergency Department

4. The original human-interest story

The report describing Polly, Katy Dessick, the child, and facility dogs working at New York hospitals.

New York Post: How dogs at NYC hospitals are helping sick kids heal

About the Author

Dr. Daniel Cham is a physician, medical consultant, and entrepreneur working at the intersection of healthcare technology, practice management, and medical billing. He is the founder of OnnX, an initiative exploring how upstream intelligence may help independent medical practices identify and reduce preventable administrative work.

His focus is on translating healthcare's operational challenges into practical questions, evidence-informed improvements, and better experiences for physicians, staff, and patients.

Connect on LinkedIn.

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Knowledge drives progress when we challenge accepted practices, learn from evidence, and turn useful insights into better healthcare operations.

Disclaimer: This article is for general educational and informational purposes only. It is not medical, legal, billing, coding, compliance, or financial advice. Requirements and circumstances vary. Consult qualified professionals for advice on specific situations.

#Healthcare #MedicalBilling #HealthcareAI #RevenueCycleManagement #PhysicianEntrepreneur #PracticeManagement #PatientExperience #HealthTech #HealthcareInnovation #OnnX #IndependentPractice #AdministrativeBurden #PatientCenteredCare

Sunday, October 18, 2026

Peyton Stewart Rang the Bell After a Life-Saving Transplant. What If Healthcare’s Next Breakthrough Is Fixing What Happens Before the Claim?

An 11-year-old’s remarkable journey reveals what great care looks like. It also raises an uncomfortable question: why is healthcare so much better at treating complex problems than preventing avoidable administrative ones?

“Patient safety, physician expertise and the humanity of clinical practice must guide how these tools are developed and used.” — Joint statement from six U.S. medical societies, reported October 1, 2026.

The boy who wanted to run the hospital

Most children leaving a hospital after a life-threatening illness have a simple wish: go home, see their friends, return to school, and reclaim the ordinary childhood that illness interrupted.

Peyton Stewart had those dreams, too.

But the 11-year-old from Gulfport, Mississippi, had one additional ambition. After surviving a rare genetic disorder and receiving a life-saving transplant, he announced that he wanted to become the future CEO of the hospital that helped save his life.

Naturally, he planned to play in the NBA first.

That is a career plan with a rather ambitious succession strategy.

Behind the humor is a remarkable story about family, medicine, and the kind of human connection that can make a frightening healthcare experience feel a little less frightening.

Peyton began experiencing recurring chalazions, or eyelid cysts, when he was just two years old. Even after surgery, the problems returned. In late 2023, while fighting multiple infections, his family learned that his hemoglobin level was dangerously low.

Then, in February 2024, a sinus infection was followed by seizures. Peyton was taken to a local hospital, where clinicians recognized the need for specialized care. He was transferred to Manning Family Children’s Hospital in New Orleans.

There, physicians eventually identified a rare condition called STAT1 gain-of-function, which caused his immune system to attack his own red blood cells.

The treatment journey was demanding. One crucial part of the solution came from an extraordinary source: Peyton’s older brother, Acie Stewart, who was a suitable match to donate bone marrow for the transplant.

Their mother, Laurie Stewart, faced the emotional burden of having two children involved in a serious medical journey at the same time. Their father, Christopher Stewart, and grandmother, Yvonne Hill, also watched Peyton navigate an experience no child should have to endure.

The transplant ultimately succeeded.

After spending months in the hospital, Peyton celebrated his recovery with a bell-ringing ceremony marking the anniversary of his transplant. His family and hospital staff gathered to celebrate a future that had once seemed uncertain.

Peyton thanked Acie for giving him a second chance at life.

He also thanked the hospital staff for helping him through difficult days, making him smile, and supporting his mother.

One detail stood out. Peyton remembered how Lucio “Lou” Fragoso, president and CEO of Manning Family Children’s Hospital, visited him and played basketball with him.

Fragoso did not simply appear as a distant executive. He became someone Peyton could look forward to seeing.

Peyton told PEOPLE that the visits cheered him up on his hardest days. He also noticed that the CEO visited other children, not just him.

That mattered.

Peyton wanted to do the same one day: help children smile on difficult days, laugh on good ones, and stay healthy.

His ambition to lead the hospital was not about an impressive title. It was about becoming the kind of leader who made a difference to people facing frightening circumstances.

As Peyton put it, he wanted to become the hospital’s CEO someday, but only after his NBA career.

Fair enough. Even healthcare leadership could use a little more long-range planning.

The real lesson is not that healthcare needs more heroes

Peyton’s story deserves to be celebrated for what it is: a child’s recovery, a brother’s generosity, a family’s endurance, and a medical team’s work.

It would be wrong to use his experience to suggest that billing problems affected his treatment. The reporting does not establish that.

But his story does invite a broader question about healthcare.

What would happen if the systems surrounding compassionate care were designed to protect more of the time, attention, and resources that compassionate care requires?

A physician can make the right diagnosis and still spend hours completing administrative work. A nurse can deliver excellent care and then spend time chasing missing information. A practice administrator can coordinate a complex visit only to discover that a document, authorization, or data field is missing when the claim is submitted.

The patient sees the appointment, the treatment, and the people who help.

The practice also sees the paperwork, portals, forms, handoffs, corrections, and follow-up work that keep the operation moving.

These are two views of the same healthcare system.

And sometimes, the second view is a lot less inspiring.

A claim can be rejected because information is incomplete. A prior authorization can require additional documentation. A payer may request clarification. A staff member must locate the information, correct the record, resubmit the claim, and monitor the result.

One task becomes three. Three become six. Six become an inbox that appears to reproduce overnight.

The frustrating part is that some of this work is necessary. Healthcare is complicated, clinical documentation must be accurate, and payers have legitimate reasons to verify coverage and medical necessity.

But necessary verification and avoidable rework are not the same thing.

The industry should not confuse the two.

The contrarian argument: a denial may be the last visible symptom, not the first failure

Medical billing discussions often begin at the point where a claim is rejected, delayed, underpaid, or denied.

That is understandable. A denial is visible. It has a status, a dollar amount, a deadline, and often a person assigned to fix it.

But visibility does not tell us where the problem began.

Consider a hypothetical example.

A patient’s insurance information changes. The update is recorded in one system but does not reach another. An authorization requirement is missed. The clinical encounter takes place, but a necessary detail is not documented in the expected location. The billing team later discovers the gap and begins a chain of corrections.

The denial or delay may appear at the end of the process. The conditions that made it likely may have appeared much earlier.

That distinction matters because a practice can become remarkably efficient at fixing the same problem repeatedly without becoming any better at preventing it.

Imagine a restaurant that burns every tenth order. Management buys a faster fire extinguisher, trains staff to respond more quickly, and celebrates a shorter recovery time.

Those improvements may be useful.

But eventually someone should ask why the kitchen keeps catching fire.

Healthcare billing deserves the same question.

Are we improving the process, or merely improving our ability to recover from a process that keeps breaking?

Not every denial is preventable. Some result from payer decisions, coverage exclusions, medical-necessity disputes, policy changes, or legitimate documentation requirements. No software can eliminate every disagreement or guarantee payment.

But that does not mean all administrative rework should be accepted as inevitable.

When the same categories of errors recur, practices should examine where information first becomes incomplete, inconsistent, inaccessible, or disconnected.

The goal is not to blame the person who submits the claim. It is to understand why that person is repeatedly asked to repair a problem that may have started somewhere else.

The hidden cost of “just one more correction”

In a busy independent clinic, administrative friction rarely arrives as one enormous crisis.

It arrives as small interruptions.

A missing insurance detail.

A chart note waiting for clarification.

An authorization that needs another attachment.

A claim that must be corrected.

A payer portal that does not agree with the practice management system.

A message asking someone to confirm what was already entered somewhere else.

Each interruption seems manageable. Collectively, they consume attention, delay work, and create opportunities for further mistakes.

There is a hidden economic cost, too.

Staff time spent correcting preventable errors is time unavailable for other work. Delayed claims can complicate cash-flow planning. Repeated follow-up increases the cost of collecting revenue. Physician time spent navigating administrative tasks is time not spent on clinical work, practice development, or simply going home on time.

That last point deserves more attention.

A clinic can appear busy, productive, and fully staffed while quietly relying on people to compensate for weaknesses in its systems.

The practice may not be short of effort. It may be short of continuity.

And effort is expensive when the same work must be done twice.

The prior authorization problem is a warning sign

Prior authorization illustrates the scale of the administrative burden, although it is not identical to medical billing and should not be treated as the cause of every claim denial.

The American Medical Association has reported substantial physician concerns about prior authorization, including treatment delays, administrative workload, and effects on physician burnout.

In its 2024 survey of physicians, the AMA reported that 94% of respondents said prior authorization delays care, 78% said it sometimes leads patients to abandon treatment, and physicians and their staff spent an average of about 13 hours per week completing prior authorization work. These findings reflect surveyed physicians’ experiences, not a universal measurement of every practice.

The broader point is hard to ignore: administrative processes can consume meaningful clinical capacity even when the people doing the work are competent and committed.

Policy changes are also moving toward greater electronic access to prior authorization information and more transparency around payer decisions. The Centers for Medicare & Medicaid Services has established interoperability and prior authorization requirements intended to improve processes and reduce burden for affected payers and providers.

Those changes matter. But a new electronic pathway does not automatically create a clean, complete, and consistent set of information at the point where a clinical or administrative decision begins.

Digitizing a fragmented process can make it faster. It can also make the fragmentation harder to see.

Automation is not the same as prevention.

A faster way to submit incomplete information is still a faster way to submit incomplete information.

The uncomfortable question: why do we fix the claim instead of examining the chain?

Think about the journey from scheduling to payment.

Insurance details must be collected and verified. Eligibility must be understood. Referrals and authorizations may be required. The clinical encounter must be documented. Coding must accurately reflect the services and applicable rules. The claim must be assembled and submitted. Payment must be reconciled, and denials or underpayments must be addressed.

These steps are connected in theory.

In practice, the information may pass between people, software systems, payer portals, and workflows that do not always communicate cleanly.

A front-desk employee may enter information that a clinician cannot easily see. A clinician may document a relevant detail that the billing team struggles to locate. A biller may discover a missing item only after the encounter is complete. A practice administrator may spend the afternoon coordinating a correction that nobody intended to create.

Each person can do their job correctly within the boundaries of their own system while the overall process still fails.

That is the problem with looking only at individual performance.

Sometimes the person is not the problem. The handoff is.

And sometimes the handoff is not even the first problem. The information may have been captured incompletely at the beginning.

This suggests a more useful way to think about revenue-cycle performance:

Data quality: Was the information accurate, complete, and current when it was first collected?
Continuity: Did the information reach the people and systems that needed it?
Timing: Was a problem detected before it caused downstream work, or only after a claim was submitted?
Ownership: Was it clear who needed to act when information was missing or inconsistent?
Learning: Did the organization prevent recurrence, or simply close the latest ticket?

These questions shift attention from the final claim to the entire chain of events.

They also reveal why adding another dashboard may not be the answer.

A dashboard can tell a practice that denials are rising. It may not explain why the same missing information keeps recurring across the front desk, clinical documentation, and billing.

A report can identify a backlog. It may not remove the underlying cause.

And another alert can make a problem more visible without making the work any easier.

Healthcare does not necessarily need more notifications telling people that they have work to do. It needs better ways to make sure the right information is available before that work becomes a correction.

The next breakthrough may be upstream, not downstream

Much of healthcare technology focuses on what happens after information has been captured: reviewing notes, suggesting codes, processing claims, identifying denials, or automating follow-up.

These capabilities can be valuable. They can reduce repetitive work and help teams manage complex operations.

But there is another opportunity.

What if the system could help prevent incomplete or inconsistent information before it enters the downstream billing process?

That does not mean forcing clinicians to complete endless forms or turning every patient encounter into a data-entry exercise.

In fact, that would be precisely the wrong lesson.

Physicians should not have to become full-time clerks to make software work. A technology designed to reduce administrative burden should not create a new administrative burden in the process.

The better goal is to make the right information easier to capture, structure, validate, and share at the appropriate point in the workflow.

For example, a well-designed process might flag a missing required field before a claim is submitted, identify inconsistent insurance information before it propagates, or make relevant documentation easier for authorized team members to locate.

The system should distinguish between information that is genuinely missing and information that simply resides somewhere different than expected.

It should also respect clinical judgment. Automation should not invent documentation, alter clinical meaning, or pressure clinicians to record something that did not occur.

A useful system helps people complete the work accurately. It does not encourage them to manufacture a cleaner record.

This is where responsible AI may have a meaningful role: not as a magical machine that guarantees reimbursement, but as a tool that can support data validation, identify patterns in recurring errors, surface missing information, and reduce avoidable manual handoffs.

The important question is not whether AI can generate another output.

It is whether the output helps prevent the next unnecessary correction.

A better test for healthcare AI

Healthcare has no shortage of impressive demonstrations.

A tool summarizes a chart. Another drafts a note. Another predicts a denial. Another generates a report that explains why last month’s process failed.

All of these may have value.

But practice owners should ask a more demanding set of questions before adopting a tool.

Does it remove work, or merely move work somewhere else?

Does it reduce the number of times staff must touch the same issue? Does it catch errors early enough to change the outcome? Does it integrate with the existing workflow, or force people to maintain another screen? Does it improve the completeness of information without compromising clinical accuracy or privacy?

And, critically, can the practice measure the difference?

Useful measures might include:

The percentage of claims accepted on the first submission.
The frequency of recurring denials by root cause.
The number of manual corrections required per claim.
The time between identifying a missing item and resolving it.
The staff hours spent on avoidable rework.
The time from service delivery to claim submission.
The percentage of identified errors prevented before submission.

No single metric tells the whole story. First-pass acceptance, for example, should not be improved by submitting questionable claims or sacrificing accuracy. Measures need to be interpreted alongside compliance, clinical integrity, payer requirements, and the patient experience.

But measurement creates something more valuable than another technology demo: evidence that the workflow is actually improving.

If a system reduces clicks but leaves the number of corrections unchanged, the practice should know that.

If it improves first-pass acceptance but increases documentation burden, the trade-off should be visible.

If it catches a problem earlier, the practice should be able to determine whether that intervention saves time or improves the outcome.

The right question is not “How much AI did we deploy?” It is “How much unnecessary work did we eliminate?”

That is a far more useful conversation for an independent clinic trying to protect both its finances and its people.

What Peyton’s story can teach us about the business of care

Peyton remembered that the hospital staff made him feel at home. He remembered activities that let him remain a child during a difficult experience. He remembered a CEO who took time to play basketball with him.

These moments did not replace the medical treatment. They helped make the experience human.

It would be simplistic to say that kindness alone makes healthcare excellent. Patients need competent clinicians, appropriate treatment, safe facilities, reliable operations, and adequate resources.

But the human side of care is not an optional decoration added after the important work is finished.

It is part of what patients and families experience.

That creates a practical responsibility for healthcare organizations. The systems surrounding clinical care should support the people delivering it rather than routinely consuming their attention with avoidable friction.

This includes revenue-cycle operations.

Billing is not the opposite of compassion. Clinics need to be paid accurately and on time to remain viable, compensate staff, maintain services, and care for future patients.

The problem is not that billing exists. The problem is when unnecessary administrative work absorbs resources that could be used more productively.

An independent clinic cannot simply absorb unlimited unpaid labor. It cannot hire a new person every time a process becomes more complicated. It cannot afford to treat every recurring error as an unavoidable cost of doing business.

Nor should it have to choose between financial sustainability and a reasonable workload for its staff.

A healthier revenue cycle should protect both the clinic’s financial health and its capacity to care.

That is not a sentimental argument. It is an operational one.

What independent clinics can do now

A practice does not need to wait for a new AI product to start improving its process. A few focused steps can help reveal where avoidable rework begins.

1. Stop treating every denial as an isolated event

Review recurring denials and corrections by category. Look for patterns involving eligibility, authorization, documentation, coding, claim data, and payer-specific requirements.

The purpose is not to create a longer spreadsheet. It is to identify repeatable problems that deserve a repeatable solution.

2. Trace the problem backward

When a claim requires correction, ask where the missing or inconsistent information first appeared.

Was it never collected? Was it entered incorrectly? Did it fail to transfer between systems? Was it available but difficult to locate? Was the requirement unclear?

A correction log becomes much more useful when it helps identify the origin of the problem.

3. Separate necessary review from preventable rework

Some claims need additional scrutiny. Some clinical situations are complex. Some payer decisions require appeals.

Do not label all manual work wasteful.

Instead, identify tasks that repeatedly occur because a known requirement was missed, a field was incomplete, or information was unnecessarily duplicated.

Those are stronger candidates for process redesign and automation.

4. Fix the handoff, not just the person

If front-desk staff, clinicians, billers, and administrators repeatedly chase the same information, examine how that information moves between them.

Clarify responsibilities. Reduce duplicate entry where possible. Make requirements visible at the appropriate stage. Establish a clear path for resolving exceptions.

The objective is to make the correct action easier, not to create another checklist that everyone must remember to check.

5. Measure whether the fix worked

Choose a small number of meaningful baseline measures and review them after making a change.

Did the correction rate decline? Did first-pass acceptance improve? Did staff spend less time chasing missing information? Did the change introduce new problems?

If the answer is unclear, the practice may have implemented a tool without validating its impact.

6. Keep the human being in the loop

AI can assist with validation and pattern detection, but clinical meaning, medical necessity, coding decisions, and compliance obligations require appropriate human oversight.

Automation should make accurate work easier, not make questionable work easier to submit.

These steps are not glamorous. They will not produce a dramatic conference demonstration.

But a clinic that eliminates one recurring source of rework may gain something more useful than a flashy demo: time, predictability, and a clearer view of its operations.

Where OnnX fits into this conversation

As a physician and founder, I believe medical billing deserves a different starting point.

The industry often asks how to process claims faster, identify denials sooner, or automate more of the work after a problem appears.

Those are important questions.

But I believe we should also ask a question that comes earlier:

How much avoidable correction can we prevent before the claim exists?

That question is central to the problem OnnX is being developed to address.

OnnX is an AI-powered medical billing SaaS for small and medium-sized clinics. The underlying thesis is that some revenue-cycle friction begins upstream, when clinical and operational information is captured, structured, or transferred between disconnected workflows.

The opportunity is to help make that information more complete, consistent, and usable before missing details turn into another round of manual correction.

This is a product direction, not a claim that every billing problem can be eliminated or that the system guarantees reimbursement. Payer policies, coverage decisions, clinical complexity, and other factors will continue to matter.

The goal is more practical: reduce avoidable friction, improve the quality of information moving through the revenue cycle, and help independent clinics spend less time repairing preventable problems.

AI should not replace physicians. It should not ask clinicians to sacrifice judgment for a cleaner spreadsheet. And it should not become another layer of software that staff must feed, monitor, and correct.

The technology should earn its place by making the work better.

If it cannot demonstrate that, the clinic should be skeptical.

After all, independent practices do not need another expensive way to discover that they have a problem. They need practical ways to solve the problems that consume their time and threaten their financial stability.

The real measure of progress

Peyton Stewart’s story ends, for now, with a bell, a family celebrating, and a boy looking forward to basketball, school, and a future he once could not take for granted.

His recovery is a reminder of what healthcare can make possible when medical expertise, family support, and human connection come together.

The lesson for healthcare operations is not that every process can be made perfect. It cannot.

It is that the systems surrounding care should be judged by whether they help people do their work well, reliably, and without unnecessary obstacles.

A denial is important. A delayed payment is important. An incomplete chart is important.

But each is also an opportunity to ask whether the visible problem began earlier in the chain.

Maybe the next improvement in medical billing will come from a faster claims engine. Maybe it will come from better automation of follow-up. Those tools have a place.

But another breakthrough may come from something less dramatic: capturing the right information at the right time, making it available to the right people, and preventing a predictable mistake from becoming another afternoon of rework.

No trumpet fanfare. No futuristic robot in a white coat. No new dashboard with 47 colors.

Just fewer avoidable corrections and more time for people to do the work that matters.

That would be progress worth measuring.

And if Peyton Stewart eventually becomes a hospital CEO after his NBA career, perhaps he will remember the same lesson his hospital experience revealed: leadership is not just about running an institution. It is about building an environment where people can help others through their hardest days.

Healthcare’s administrative systems should be designed with that same ambition.

One question for physicians, practice owners, and healthcare operators

If you could eliminate one recurring administrative problem from your practice tomorrow, which would give your team the most time back for patient care?

Share your experience in the comments. Your answer may help another clinic recognize a problem it has mistakenly accepted as normal.

If you believe healthcare needs less avoidable rework and more time for patients, repost this article so the conversation reaches more physicians, clinic owners, practice administrators, and medical billers.

A free resource for independent clinics: Find Revenue Cycle Made Simple for Independent Clinics in the Featured section of my LinkedIn profile. It is available without a signup.

About the Author

Dr. Daniel Cham is a physician, healthcare strategist, and founder of OnnX, an AI-powered medical billing SaaS focused on reducing administrative friction for small and medium-sized clinics. His work explores healthcare operations, medical billing, clinical data quality, and responsible technology, with an emphasis on practical improvements that protect physician time and support patient-centered care.

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Disclaimer

This article is for educational and informational purposes only. It does not constitute medical, legal, coding, billing, reimbursement, or compliance advice. Healthcare organizations should consult qualified professionals and applicable payer and regulatory requirements when making operational or billing decisions. Peyton Stewart’s story is discussed as a human-interest example; the reporting cited does not establish that billing or administrative problems affected his treatment.

Sources and further reading

#MedicalBilling #RevenueCycleManagement #HealthcareOperations #PhysicianEntrepreneur #IndependentPractice #HealthcareInnovation #PracticeManagement #PatientCenteredCare #HealthcareAI #OnnX