Daniel Cham MD

Thursday, October 8, 2026

Mike and Kelsey Kennedy’s Fight to Reunite Juliette and Kendall: The Hidden Contradiction in Modern Healthcare

Two premature twins. Two Boston hospitals. One family caught between medical necessity and the desire to keep their daughters together. Their story raises an uncomfortable question about how healthcare systems treat the people who depend on them.

“making a broken workflow faster doesn’t fix the workflow.” — Richard Atkin, CEO of Greenway Health, The New Rules of Leadership in the Era of AI

Healthcare can perform extraordinary medical procedures and still leave families struggling to navigate the system around them. For independent physicians, the same operational question takes another form: are we solving administrative problems, or simply getting better at managing them?

Two babies. Two hospitals. One question healthcare cannot ignore.

Mike and Kelsey Kennedy of Canton, Massachusetts, are facing a situation no parent prepares for.

Their twin daughters, Juliette and Kendall Kennedy, were born at just 24 weeks of pregnancy.

Both babies needed intensive medical care. Kendall was born with spina bifida and underwent surgery at Boston Children’s Hospital. Juliette required heart surgery and received care at Brigham and Women’s Hospital.

The sisters who entered the world together were receiving treatment in separate hospitals.

At home, the Kennedys also have two young children, Callan and Aurora. Their parents must balance the needs of all four children while navigating the uncertainty surrounding their newborn daughters.

In an October 6 report, CBS Boston quoted Mike Kennedy describing the helplessness of watching his daughters surrounded by medical equipment.

“It’s difficult. They’re in a glass enclosure with feeding tubes and breathing tubes.”

For a parent, the hardest part is not necessarily understanding that specialized medical care is necessary. It is knowing that your children need you while being unable to be in two places at once.

The family has expressed gratitude for the doctors and nurses caring for their daughters. Their request to reunite the twins exists alongside the clinical realities of medical stability, specialist needs, and hospital capacity.

This is not a story about blaming clinicians for difficult decisions. Nor does it establish that either hospital has provided inappropriate care.

It is a story about the human experience surrounding medical treatment.

And it raises a question worth asking throughout healthcare:

When a system becomes complicated, who carries the burden of making it work?

For the Kennedy family, the answer is painfully personal.

For a physician running an independent practice, the answer might be a receptionist correcting insurance information, a nurse chasing an authorization, a biller resubmitting a rejected claim, or a physician finishing documentation after the last appointment.

Different circumstances. Different stakes.

But a shared operational question remains: How much unnecessary work are we asking people to absorb because our processes are not working as well as they should?

Healthcare has a strange talent for making hard things possible and simple things difficult

Modern medicine can perform extraordinary procedures.

It can care for babies born extremely prematurely. It can coordinate complex surgeries. It can help patients recover from conditions that once offered little hope.

These achievements deserve recognition.

Yet healthcare professionals and patients still encounter administrative processes that are confusing, repetitive, and unnecessarily difficult to navigate.

A patient completes an intake form, only to provide the same information again.

A physician finishes an encounter, but the documentation still requires additional work.

A billing team submits a claim, receives a rejection, investigates the cause, corrects the record, and submits it again.

A practice manager spends an afternoon figuring out why a task marked complete never produced the expected result.

Some of this work is necessary. Some reflects legitimate clinical, contractual, or regulatory requirements.

But some deserves a more uncomfortable question.

Why are we still doing this?

Not “How can we do it faster?”

Not “Which software can help us manage it?”

Not even “Who can we hire to handle the additional workload?”

First ask why the work exists.

Healthcare sometimes behaves as if every recurring problem deserves another layer of process.

A missing document creates a reminder. The reminder creates a work queue. The work queue needs monitoring. The monitoring creates a dashboard. The dashboard generates another report.

Congratulations. We have successfully built a small administrative civilization around a missing document.

The humor is intentional. The cost is real.

Every additional step consumes attention. Every correction competes with other work. Every unclear handoff creates an opportunity for something to be delayed or forgotten.

The answer is not to eliminate all process. Reliable healthcare needs structure, documentation, oversight, and accountability.

The answer is to distinguish necessary complexity from complexity we have simply learned to tolerate.

A process that requires heroic effort every day may not be a well-designed process. It may be a process that has learned to survive.

That distinction matters enormously for small medical practices, where staff capacity and physician time are limited.

The contrarian truth: Your billing team may not have a billing problem

Here is a thought that challenges conventional revenue-cycle thinking:

Some medical billing problems begin before billing ever starts.

Imagine a patient arrives for an appointment.

An insurance detail is entered incorrectly. Nobody notices.

The physician provides appropriate care. The encounter is completed.

The billing team receives the information and submits the claim.

The payer rejects it because the information does not match its records.

Now the billing team must investigate. Someone checks eligibility. Someone contacts the patient. Someone corrects the record. Someone resubmits the claim. Someone monitors the result.

One small error has created a chain of work.

And the billing team may be blamed for the delay.

This is the operational equivalent of blaming the fire department for being busy while ignoring the faulty wiring.

The analogy is imperfect, but the lesson is important: the person who discovers a problem is not necessarily the person or process that created it.

The original issue may have started during registration, eligibility verification, documentation, authorization, charge capture, coding, or an information handoff.

The billing team often sees the consequences rather than the beginning.

A practice can become exceptionally good at correcting errors without becoming any better at preventing them.

That is why I believe medical billing is partly a data-quality problem, not simply a billing-workflow problem.

A team can improve its downstream processes while leaving the upstream source of recurring errors untouched.

It can get faster at fixing the same mistakes.

It can buy software to manage the additional work.

It can hire more people to keep the process moving.

Or it can investigate why the work keeps returning.

The last option is less glamorous than launching a new technology platform.

It may also be the more valuable place to begin.

The statistics: Administrative burden is not just an inconvenience

Administrative friction is not merely a collection of frustrating anecdotes.

Prior authorization provides a measurable example.

The American Medical Association has documented substantial administrative burdens associated with prior authorization in its physician surveys.

Its survey findings reported in 2026, based on the 2025 survey, included the following:

40 prior authorization requests per physician per week, on average.
Approximately 13 hours of physician and staff time per week devoted to those requests.
95% of surveyed physicians reported that prior authorization delays access to necessary care.
79% said patients sometimes abandon treatment because of authorization challenges.
94% said prior authorization contributes to physician burnout.

These figures reflect survey responses. They are not proof that every authorization is unnecessary or that every delay causes harm. Prior authorization can serve legitimate purposes, and practices must distinguish appropriate clinical review from avoidable administrative friction.

But the reported workload deserves attention.

Thirteen hours is not an abstract number. It represents time that physicians and staff say they spend navigating authorization requirements.

Some of that work requires clinical expertise. Some requires payer communication. Some may involve information that the practice cannot control.

The operational question is how much time is spent on meaningful review and how much is spent correcting incomplete information, chasing responses, duplicating work, or checking the status of requests.

The distinction matters.

The objective is not to eliminate necessary work. It is to stop treating avoidable work as inevitable.

Three expert perspectives that challenge conventional thinking

These perspectives are drawn from published work by established healthcare experts and organizations. They are summaries of their work, not new interviews or endorsements of OnnX.

Expert 1: Richard Atkin — Do not automate yesterday's mistakes

In his October 7, 2026, article for Greenway Health, CEO Richard Atkin argues that healthcare leaders should not confuse the speed of technology adoption with meaningful progress.

His central point is particularly relevant to medical billing: before automating a process, leaders should examine why it exists, which steps still serve a purpose, and where human judgment is valuable.

Consider a workflow that repeatedly produces incomplete claims.

Automating claim submission might make the process faster. But if the underlying information remains incomplete, the organization may simply produce errors more efficiently.

That is not transformation. It is acceleration.

Practical advice: Before implementing automation, map the workflow, identify recurring failure points, and establish the outcome you want to improve. Then measure whether the technology delivers that improvement.

Expert 2: The American Medical Association — Administrative work affects clinical capacity

The AMA's prior authorization research documents physicians' concerns about delays, workload, and the effect of administrative requirements on patient care.

The lesson for independent practices is that administrative requirements should be evaluated not only by whether a task was completed, but also by the time and resources needed to complete it.

A process can be technically compliant while still imposing avoidable duplication.

Practical advice: Track authorization turnaround times, identify recurring payer-specific obstacles, clarify ownership of outstanding requests, and establish escalation procedures.

Where requirements are necessary, make compliance as predictable as possible. Where duplication exists, investigate whether it can be removed.

Expert 3: The Agency for Healthcare Research and Quality — Coordination requires clear information and accountability

The Agency for Healthcare Research and Quality describes care coordination as the deliberate organization of patient-care activities and the sharing of information among the people responsible for care.

This principle also offers a useful way to examine administrative handoffs.

A receptionist, physician, coder, biller, and external billing company may each complete their assigned task. Yet the overall process can still fail if nobody owns the outcome.

Practical advice: For every handoff, define the information required, the next responsible person, what counts as completion, and how completion will be verified.

A message sent is not necessarily a task completed.

A claim submitted is not necessarily a claim resolved.

A checkbox ticked is not necessarily a problem solved.

Five medical billing myths that deserve retirement

Myth 1: More follow-up always means better revenue cycle management

Follow-up is essential when claims are delayed, denied, or unpaid.

But repeated follow-up can also reveal a process that is not working properly.

If staff continually contact payers because documentation is incomplete, claim status is unclear, or information must be corrected repeatedly, more follow-up may treat the symptom rather than the cause.

Better approach: Measure why follow-up is necessary and determine which causes are preventable.

Myth 2: Every denied claim is a billing department failure

The billing team may discover the denial without having caused it.

The original problem could involve registration, eligibility, authorization, documentation, coding, contractual requirements, or payer decisions.

Blaming the final person in the chain is convenient. It is not necessarily accurate.

Better approach: Trace recurring denials to their origin and involve the team responsible for that step.

Myth 3: More software means fewer problems

Software can help with validation, monitoring, reporting, and repetitive tasks.

But it cannot automatically fix unclear responsibilities, unreliable source data, or poorly designed processes.

Automating a flawed workflow can produce the same errors at greater speed.

Better approach: Understand the process before selecting the tool, then measure whether the tool improves accuracy, reliability, and total workload.

Myth 4: Outsourcing makes billing someone else's problem

An external billing company can provide expertise and capacity.

But outsourcing does not remove the practice's need to monitor performance, protect patient information, understand financial results, and maintain appropriate oversight.

A contract is not a substitute for accountability.

Better approach: Establish clear reporting standards, service expectations, security requirements, and escalation procedures.

Myth 5: A submitted claim is a completed claim

Submission is a milestone, not the final outcome.

A claim can still be rejected, denied, underpaid, delayed, or incorrectly adjudicated.

Better approach: Monitor the claim through adjudication, payment, reconciliation, and appropriate resolution.

Measure outcomes rather than celebrating activity.

Seven practical steps to reduce administrative waste

You do not need a complete technology overhaul to start improving your revenue cycle.

Begin with one recurring problem that matters.

Step 1: Map what really happens

Choose a process, such as eligibility verification, claim submission, denial management, or payment posting.

Document the actual sequence of events.

Who touches the information? Which systems are involved? Where does the work stop? What causes it to return to an earlier step?

Ask staff where they spend time correcting information, waiting for responses, or following up on tasks that should already be resolved.

Do not map the process as management imagines it. Map it as employees experience it.

Step 2: Establish a baseline

Before changing anything, determine how the process performs today.

Useful measures include:

First-pass claim acceptance rate.
Initial denial rate, using a clearly defined denominator.
Days in accounts receivable.
Receivables aged beyond 60 or 90 days.
Time from encounter to claim submission.
Average denial resolution time.
Staff hours spent on rework.
Net collection rate, calculated consistently.
Outstanding authorization requests.
Frequency of corrected documentation.

Benchmarks vary by specialty, payer mix, contract terms, and practice size. Avoid comparisons unless the definitions and circumstances are comparable.

Step 3: Identify recurring errors

Review a representative sample of rejected and denied claims.

Classify the causes.

Are problems concentrated in eligibility, documentation, coding, authorization, payer routing, timely filing, or payment discrepancies?

Separate preventable problems from those requiring payer intervention or clinical judgment.

Prioritize by frequency, financial impact, patient consequences, and effort required to fix the issue.

Step 4: Improve information at the source

If inaccurate information enters the system, downstream correction becomes expensive.

Review whether registration captures the necessary information, eligibility is checked at the appropriate time, and missing data can be identified before claim submission.

Use validation rules where appropriate.

For clinical documentation, preserve the physician's judgment and the integrity of the medical record.

The goal is not more documentation for its own sake. It is accurate, complete information that supports appropriate care and reimbursement.

Step 5: Make handoffs explicit

For each recurring task, establish five things:

Who owns the next action?
What information must be transferred?
What counts as completion?
How is completion verified?
When should the issue be escalated?

This is particularly important when multiple departments, external billers, or separate technology systems are involved.

A task should not disappear simply because it has moved to another queue.

Step 6: Automate selectively

Automation may support data validation, routine claim-status monitoring, work queues, reminders, and identification of recurring patterns.

Start with stable, well-defined processes.

Keep human review where clinical interpretation, ambiguous payer requirements, or consequential decisions require judgment.

Test tools against real cases. Monitor errors introduced by the tool as well as time saved.

A successful implementation should reduce total work, not merely move it from one employee to another.

Step 7: Review results monthly

Choose a small set of measures that reflect your goals.

Review what improved, what deteriorated, and what remains unexplained.

Ask staff whether the new process is easier to use. Look for unintended consequences, such as additional data entry or confusing exceptions.

If the change does not improve the outcome, revise it.

Improvement is a continuous process, not a software installation.

The pitfalls: How well-intentioned improvements go wrong

Automating before understanding the cause. A tool may flag missing information without explaining why it is repeatedly absent.

Optimizing one department at the expense of another. A workflow that makes billing easier may create more work for clinicians or front-desk staff.

Measuring activity instead of outcomes. Claims submitted, calls made, and tasks completed reveal workload. They do not necessarily demonstrate payment, accuracy, or resolution.

Ignoring the people who perform the work. Employees often understand exceptions and workarounds that management does not see. Involve them before redesigning the workflow.

Treating every denial as preventable. Some denials reflect contractual disputes, payer decisions, medical-necessity determinations, or factors beyond the practice's direct control.

Confusing speed with quality. A faster process is not an improvement if it produces inaccurate claims, unreliable records, or additional compliance risks.

The objective is reliable work with less unnecessary effort.

Legal and ethical considerations: Efficiency has boundaries

Medical billing improvements must respect applicable HIPAA privacy and security requirements, accurate documentation, truthful claims, coding rules, payer contracts, and relevant federal and state laws.

Several principles deserve particular attention.

Documentation integrity: Never alter clinical documentation merely to support reimbursement. Corrections and amendments should follow appropriate policies.

Coding accuracy: Automation should support qualified review and established coding rules. Financial targets must never override clinical facts.

Privacy and security: Assess access controls, data handling, retention, safeguards, and contractual obligations before adopting a technology platform. Where applicable, establish appropriate business associate agreements.

Human oversight: Ensure that consequential automated recommendations can be reviewed, especially when they affect claims, appeals, or patient balances.

Vendor accountability: Define responsibilities, audit rights, security requirements, and procedures for incidents and disputes.

Patient communication: Financial processes should not mislead patients or obscure the status of a claim.

Efficiency is not an excuse to cut corners.

The objective is to complete legitimate work accurately, securely, and with less unnecessary effort. Practices should consult qualified legal, coding, and compliance professionals when evaluating specific requirements.

What healthcare founders should learn from this

Healthcare technology founders often begin with a reasonable question:

What task can we automate?

But there is a more revealing question:

Why does this task exist in the first place?

If staff repeatedly correct missing information, perhaps the opportunity is better validation at the point of entry.

If claims repeatedly fail for the same reason, perhaps the opportunity is to prevent the error instead of speeding up resubmission.

If physicians spend hours navigating administrative processes, perhaps the opportunity is to simplify information exchange, clarify responsibility, or remove duplicate work.

Artificial intelligence can help identify patterns, organize information, and support repetitive administrative tasks.

But AI cannot guarantee accurate source data, sound process design, or appropriate human judgment.

A beautifully written summary of inaccurate information is still inaccurate.

An automated workflow that reproduces a flawed process is still flawed.

And a dashboard that shows exactly how much time staff spend correcting errors does not, by itself, prevent the next error.

Good innovation begins with observation.

Talk to physicians. Listen to staff. Examine real workflows. Identify assumptions. Test small changes. Measure whether they work.

Not every problem needs AI. Not every manual step should be removed. Not every clinical process can be standardized without accounting for individual circumstances.

The goal is not to automate everything.

It is to make the right work easier and unnecessary work less common.

Where OnnX fits into this conversation

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

My interest in this problem comes from a simple observation: many recurring billing problems have roots earlier in the process.

Information captured during registration, the completeness of documentation, the quality of eligibility checks, and the reliability of handoffs can all influence what happens later in the revenue cycle.

OnnX's direction reflects an upstream perspective.

Rather than treating every denial or correction as an isolated task, I believe practices should investigate the conditions that make those tasks necessary.

That does not mean every problem can be prevented. Payer policies, contractual disagreements, clinical complexity, and external requirements will continue to create work.

It means we should distinguish unavoidable work from work that better processes could reduce.

For independent practices, the potential value is practical: less avoidable rework, better visibility into outstanding issues, more consistent information, and more time for work that requires human expertise.

OnnX is in its validation stage. Learning from physicians and clinic owners is therefore essential.

The aim is to understand which problems matter most, where current approaches fall short, and what measurable improvement would actually look like.

That requires honest conversations, not unsupported promises.

The best starting point is not a sales pitch.

It is a question:

Where does your practice lose the most time fixing problems that should never have happened?

Frequently asked questions

Why does medical billing create so much administrative work?

The revenue cycle depends on information from registration, eligibility verification, clinical documentation, coding, authorization, payer adjudication, and payment reconciliation.

When information is incomplete, inconsistent, or delayed, staff must investigate and correct the resulting problems.

The first step is to identify recurring sources of rework rather than assume every practice has the same issue.

How can a small practice reduce claim denials?

Review a representative sample of denials, classify the causes, and prioritize frequent, preventable errors.

Improve upstream validation, clarify documentation requirements, establish ownership, and monitor whether the same problems return.

Distinguish preventable denials from payer disputes and issues outside the practice's direct control.

Can AI eliminate the need for medical billers?

No. AI can support repetitive tasks, identify patterns, and help organize information, but qualified professionals remain important for coding, payer rules, exceptions, compliance, and human review.

The appropriate goal is to automate suitable tasks while preserving necessary expertise and accountability.

What should clinic owners measure first?

Start with clearly defined measures such as first-pass claim acceptance, denial rate, days in accounts receivable, rework hours, and the age of unresolved claims.

Choose metrics that reflect your actual problems and establish a baseline before changing the process.

Does outsourcing eliminate billing responsibility?

No. External billing services may provide expertise and capacity, but practices still need appropriate oversight, performance reporting, privacy safeguards, and a clear understanding of financial results.

How does better billing support patient care?

Reliable billing processes can reduce avoidable staff rework and improve financial visibility.

Although better billing does not automatically improve clinical outcomes, reducing unnecessary administrative work can create more capacity for patient communication and other essential activities.

What is the difference between claim submission and claim resolution?

Submission means a claim has been sent to a payer. Resolution means its outcome has been appropriately addressed through payment, correction, appeal, adjustment, or another legitimate disposition.

Submission alone does not establish that the work is finished.

How can practices automate responsibly?

Define the task, establish acceptable error rates, protect patient information, test the system against real cases, and determine when human review is necessary.

Measure unintended consequences as well as benefits.

What should a practice do when the same billing error keeps returning?

Trace the error to its origin, examine the process, assign responsibility, implement a corrective action, and measure whether the error recurs.

Repeated correction without root-cause analysis can consume substantial time without improving reliability.

Can a practice improve billing without replacing its existing systems?

Yes. Begin by mapping one workflow, reviewing denial reports, identifying recurring errors, and testing a focused improvement.

Better responsibilities, data validation, follow-up procedures, and reporting may improve performance without requiring a complete technology replacement.

Final thoughts: Stop rewarding the work that should not exist

The story of Mike and Kelsey Kennedy and their daughters, Juliette and Kendall, reminds us that healthcare is experienced by people navigating uncertainty, difficult decisions, and complicated circumstances.

Their experience is distinct from the administrative challenges faced by independent practices. But it encourages an important question: what unnecessary burdens are people being asked to carry, and what can healthcare organizations reasonably do to reduce them?

For physicians, the lesson is to distinguish necessary administrative work from recurring friction that deserves investigation.

For practice owners, it is to measure outcomes rather than activity and fix root causes rather than repeatedly correct symptoms.

For founders, it is to design technology around real problems rather than assume that another layer of software is always the answer.

Healthcare does not need to make every process simple. It needs to stop making avoidable complexity someone else's daily responsibility.

Three actions worth taking today

Find the friction. Identify one recurring administrative task that consumes time without reliably moving work toward resolution.

Measure the problem. Establish a baseline, investigate the cause, and test a practical improvement before expanding it.

Protect human attention. Use better processes and appropriate technology to give physicians and staff more room for the work that genuinely requires them.

Continue the conversation

What if the next major improvement in healthcare came not from adding another tool, but from removing an unnecessary step?

Share your perspective: What is the most persistent source of administrative rework in your practice: incomplete information, payer requirements, unclear handoffs, or something else?

Join the discussion: Leave a comment describing one process you would redesign if you could change it tomorrow.

Help other physicians rethink the problem: If this perspective resonates, consider reposting the article to encourage more clinic owners and healthcare leaders to share their experiences.

Knowledge becomes useful when it leads to better questions, practical experiments, and measurable improvement.

If you are exploring ways to make medical billing more predictable or reduce unnecessary administrative work, start by examining your own workflow and identifying what deserves to change first.

P.S. Check the Featured section of my LinkedIn profile for a free resource. No signup is required.

About the author

Dr. Daniel Cham is a physician, medical consultant, and founder of OnnX, an AI-powered medical billing SaaS focused on small and medium-sized physician-owned practices. His work explores the intersection of medical billing, healthcare operations, practice management, and technology, with an emphasis on practical ways to reduce administrative friction and improve operational reliability.

Connect with Dr. Cham on LinkedIn: linkedin.com/in/daniel-cham-md-669036285.

Explore more insights

For practical perspectives on healthcare operations, physician-led innovation, medical billing, and the challenges of running a medical practice, explore these channels:

Website: drdanielcham.com
Spotify: Listen to the podcast
YouTube: Watch and subscribe
X: Follow updates
Facebook: Join the conversation
Substack: Read more insights
TikTok: Explore short-form content
Instagram: Follow on Instagram

Knowledge drives progress. Challenge unnecessary complexity, ask better questions, and turn useful insights into meaningful change.

Disclaimer

This article provides general educational information about healthcare operations and medical billing. It does not constitute legal, medical, coding, reimbursement, or compliance advice. Requirements vary by jurisdiction, payer, practice, and contractual arrangement. Consult qualified professionals for guidance specific to your circumstances.

References and further reading

1. A family navigating the realities of specialized neonatal care. CBS Boston reports on the Kennedy twins' medical needs, their treatment at separate Boston hospitals, and their parents' hope of reuniting them.

Read the CBS Boston report

2. Physician experiences with prior authorization. The American Medical Association summarizes survey findings on administrative workload, treatment delays, and physicians' reported concerns about patient care.

Read the AMA report

3. Improving coordination through information and accountability. The Agency for Healthcare Research and Quality provides resources on coordinating care and sharing information among healthcare professionals.

Explore AHRQ's care coordination resources

#Healthcare #MedicalBilling #RevenueCycleManagement #HealthcareAdministration #PhysicianLeadership #IndependentPractice #HealthcareInnovation #PracticeManagement #PhysicianBurnout #HealthTech #AIinHealthcare #OnnX

Wednesday, October 7, 2026

Maralee Lellio Wanted to Live. Why Is Healthcare So Good at Making Life Harder?

Maralee Lellio fought for more time with Andrew, Ayla and Rosa. Her story raises an uncomfortable question for physicians and healthcare leaders: Are we using AI to give human attention back to healthcare—or simply making unnecessary work faster?

“Patient safety, physician expertise and the humanity of clinical practice must guide how these tools are developed and used.” — American Medical Association, American Academy of Family Physicians, American Academy of Pediatrics, American College of Obstetricians and Gynecologists, American College of Physicians, and American College of Surgeons, September 30, 2026

Healthcare doesn't need more automation. It needs fewer problems to automate.

Maralee Lellio Wanted to Live.

Not optimize a workflow.

Not improve a dashboard.

Not reduce clicks.

Not increase productivity.

Live.

That distinction matters more than it first appears.

In 2018, Maralee Lellio was 28 years old and living in northeastern Ohio.

She was a teacher.

She was a mother.

She was married to Andrew Lellio.

She had a young daughter, Ayla Lellio.

And she imagined the future the way most young parents do.

She would keep teaching.

Ayla would grow up.

Maybe they would have another child.

There would be birthdays, school mornings, family dinners, arguments over bedtime and all the wonderfully ordinary chaos that makes a life.

Then Maralee found a lump.

She did what patients are told to do.

She sought medical attention.

She was reassured that it was likely benign.

It wasn't.

The lump grew.

It became painful.

In January 2019, at age 29, Maralee was diagnosed with Stage 2B triple-negative breast cancer.

She underwent chemotherapy and a bilateral mastectomy.

For a while, things seemed to be moving in the right direction.

Then came the headaches.

Then dizziness.

Then worsening symptoms.

A brain scan initially did not reveal cancer.

Eventually, an MRI showed a large brain tumor.

Her breast cancer had progressed to Stage IV disease.

The prognosis was devastating.

At one point, Maralee accepted that she might die within a few years.

She thought about Ayla.

She thought about the second child she and Andrew had dreamed about.

She thought about all the ordinary things she might never get to experience.

Then Andrew said something that changed her trajectory.

He told her he believed she could survive.

He reminded her that some people live far beyond the prognosis they are given.

Maralee later described the moment as a light switch turning on.

She decided:

“I'm going to live.”

Not because anyone promised her a cure.

Not because an algorithm predicted a favorable outcome.

Not because her medical record produced a green checkmark.

She decided that the future was still worth pursuing.

Her care eventually moved to Cleveland Clinic.

There, Maralee received radiation and later a PARP inhibitor, a targeted therapy particularly relevant to her BRCA1-positive cancer.

Her treatment worked.

She eventually had no evidence of active disease.

Then came the part of the story that sounds almost impossible.

Maralee wanted another child.

Her oncologist, Halle Moore, MD, supported her decision after carefully weighing the risks.

In July 2024, Maralee and Andrew welcomed their second daughter, Rosa Lellio.

Ayla became a big sister.

Maralee became a mother of two.

And she returned to teaching.

Cleveland Clinic describes Maralee today as the mother of two daughters, Ayla and Rosa, and quotes her describing life with them as “messy and perfect.” [1]

That phrase stopped me.

Messy and perfect.

Because perhaps we have spent too much time trying to eliminate the wrong kind of mess.

There is the mess of life.

And there is the mess of healthcare.

They are not the same thing.

A child waking up at 2 a.m. is messy.

A toddler spilling milk is messy.

A patient asking one more question is messy.

A physician spending an extra five minutes listening is messy.

A family changing plans because life happened is messy.

That is human.

That is life.

Healthcare should make room for that kind of mess.

Instead, we have built an enormous amount of administrative mess that nobody asked for.

Faxing.

Re-keying.

Rechecking.

Prior authorization.

Denial appeals.

Duplicate documentation.

Missing information.

Portal messages.

Payer portals.

Coding corrections.

Eligibility problems.

Authorization problems.

Claim edits.

Payment reconciliation.

Manual follow-up.

And then we proudly announce:

“Don't worry. We have AI.”

Maybe.

But before we celebrate the robot, perhaps we should ask why the robot has so much work to do.

The Healthcare Industry Has a Strange Definition of Innovation

Here is my contrarian proposition:

Healthcare does not have an automation problem nearly as much as it has a problem deciding what deserves human attention.

We keep creating complicated processes.

The processes create exceptions.

People manage the exceptions.

The exceptions create more work.

Organizations hire more people.

Technology companies build software to process the work.

Then someone adds AI.

And suddenly we call the whole thing transformation.

Sometimes it is.

Sometimes it is simply a faster way to do something we should have stopped doing.

That distinction is becoming increasingly important.

Because healthcare AI is entering almost every layer of the system.

AI documentation.

AI coding.

AI scheduling.

AI prior authorization.

AI claims.

AI denials.

AI payment posting.

AI patient communication.

AI revenue-cycle management.

AI clinical decision support.

AI everything.

The question is no longer whether AI will enter healthcare.

It already has.

The better question is:

Where should intelligence be applied?

And even more importantly:

What work should disappear before we automate it?

The Patient Is Not the Workflow

Maralee's story has almost nothing to do with medical billing.

And that is exactly why I want to start there.

I am not suggesting that medical billing caused her cancer.

It didn't.

I am not suggesting that an AI billing platform could have changed her cancer outcome.

It couldn't.

That would turn a deeply human story into a marketing gimmick.

The real lesson is much broader.

Human attention is finite.

Andrew Lellio gave Maralee attention.

He watched her.

He drove her to Cleveland Clinic.

He worked from home so he could monitor her.

He stayed beside her.

He helped her when she was too weak to get up.

He made sure she ate.

He was there.

The Cleveland Clinic team gave her attention.

Dr. Halle Moore gave her attention.

Maralee gave attention to her own future.

That attention mattered.

Now think about a typical physician practice.

A doctor opens the EHR.

There are messages.

Tasks.

Alerts.

Refills.

Forms.

Authorizations.

Denials.

Unsigned documents.

Coding questions.

Results.

Patient messages.

Administrative requests.

The patient needs attention.

But the system needs attention too.

And the system never stops asking.

That is the hidden cost of healthcare complexity.

The system competes with the patient for human attention.

And we have become remarkably good at letting the system win.

The Most Valuable Resource in Healthcare Isn't AI

It isn't data.

It isn't software.

It isn't even money.

It is attention.

Physicians have limited attention.

Nurses have limited attention.

Medical assistants have limited attention.

Billers have limited attention.

Practice managers have limited attention.

Patients have limited attention.

Caregivers have limited attention.

Everyone has a finite cognitive budget.

Yet our healthcare infrastructure behaves as though human attention were unlimited.

It isn't.

And eventually the bill comes due.

The American Medical Association's recent physician research continues to show the enormous burden of administrative work, particularly prior authorization.

Physicians report delays in care, negative effects on outcomes, patient abandonment of treatment, serious adverse events and substantial amounts of physician and staff time devoted to authorization processes.

That is not simply a workflow inconvenience.

It is an attention problem.

And attention problems eventually become patient-care problems.

Thirteen Hours Is Not a Workflow

Consider the often-cited prior-authorization burden.

Physicians and staff can spend approximately 13 hours per week dealing with prior authorization.

Thirteen hours.

That is more than one full workday.

Every week.

Imagine telling a physician:

“We're going to take one day out of your week, every week, and give it to paperwork.”

We would call that unacceptable if we described it honestly.

Instead we call it:

Administrative burden.

What a wonderfully polite phrase.

“Administrative burden” sounds like carrying a heavy backpack.

It is closer to asking a physician to run a call center between patients.

And then wondering why they are exhausted.

We Celebrate the Wrong Heroes

Healthcare has a strange cultural relationship with heroism.

We celebrate the biller who gets the impossible claim paid.

The nurse who stays late.

The physician who answers messages at 10 p.m.

The practice manager who knows exactly which payer portal to use.

The coder who finds the missing documentation.

The employee who remembers the workaround nobody wrote down.

These people are heroes.

But there is a dangerous side effect.

Organizations can become dependent on heroes.

A process breaks.

Someone fixes it.

The organization survives.

Everyone goes home.

Until it breaks again.

Then the same person fixes it.

Again.

And again.

Eventually the organization says:

“Our team is really good at handling exceptions.”

Maybe.

Or maybe your system is really good at producing exceptions.

There is a difference.

The Hero Trap

The hero trap looks like this:

Information enters the system.
Something is missing.
A human notices.
A human investigates.
A human calls someone.
A human opens another system.
A human finds the information.
A human corrects the record.
A human resubmits the transaction.
The organization celebrates the recovery.
Nobody redesigns the original process.

Then it happens again tomorrow.

That isn't resilience.

That's institutionalized rework.

And here's where AI can become dangerous.

Not because AI is dangerous by itself.

Because AI can become an extremely efficient employee inside a fundamentally inefficient architecture.

Imagine hiring the smartest assistant in the world and giving that assistant 50,000 badly completed forms.

The assistant can process them faster.

Congratulations.

You now have the world's fastest bad-form processor.

But why are there 50,000 bad forms?

That's the question.

The AI Arms Race Has a Blind Spot

The healthcare industry is racing toward AI.

Good.

We should.

AI can be transformative.

But there is a blind spot in the conversation.

We frequently assume that intelligence automatically creates simplicity.

It doesn't.

Intelligence can process complexity. It does not necessarily eliminate complexity.

That distinction should be printed on every healthcare AI pitch deck.

If documentation is inconsistent, AI can interpret it.

If claims are denied, AI can predict them.

If coding is manual, AI can accelerate coding.

If prior authorization is cumbersome, AI can automate parts of it.

All useful.

But none of those necessarily changes the underlying architecture.

The system still depends on the mess.

It simply becomes better at dealing with it.

That is not the same as removing the mess.

What If We Stopped Asking AI to Clean Up After Us?

Here's the question I would like every healthcare technology company to answer:

What if the most valuable AI doesn't clean up the mess faster?

What if it prevents the mess?

That changes everything.

Instead of:

Predict the denial.

Ask:

Why was the claim likely to be denied?

Instead of:

Automate the appeal.

Ask:

Could the original error have been prevented?

Instead of:

Make the biller faster.

Ask:

Why does the biller have to touch this transaction at all?

Instead of:

Build another dashboard.

Ask:

Why does someone need a dashboard to discover something the system should already know?

That is a much more uncomfortable conversation.

It is also where the real opportunity may be.

Healthcare Has Been Optimizing Around Noise

Healthcare organizations have become incredibly sophisticated at compensating for variability.

Different physicians document differently.

Different staff members enter information differently.

Different systems represent information differently.

Different payers require different things.

Different workflows interpret the same information differently.

Different people work around the limitations of software differently.

Then we build more software to manage the variability.

Eventually the infrastructure becomes enormous.

And everybody asks:

“Why is healthcare so complicated?”

Maybe because we built an entire economy around compensating for complexity.

This is where I believe the industry needs a reset.

Stop optimizing around noise. Remove the noise.

This Is the OnnX Thesis

This is the philosophy behind OnnX.

Healthcare billing is often treated as a workflow problem.

I believe much of the problem begins earlier.

It is a data-structure problem.

The revenue cycle receives the consequences of decisions made upstream.

Clinical documentation.

Patient information.

Coverage.

Eligibility.

Authorization.

Coding context.

Encounter details.

Operational information.

When those elements are incomplete, inconsistent or poorly structured, downstream teams inherit the problem.

The biller becomes the detective.

The coder becomes the interpreter.

The administrator becomes the coordinator.

The practice manager becomes the escalation department.

And the physician often becomes the person asked to fix documentation after the fact.

That's backwards.

The person closest to the patient should not be forced to become the cleanup crew for the revenue cycle.

Clean Claims Shouldn't Be Heroic

Think about the phrase:

Clean claim.

We celebrate it.

We measure it.

We optimize it.

But why is a clean claim considered an achievement?

Shouldn't it simply be the natural result of good information?

If the right information was captured...

If it was structured correctly...

If it was available when needed...

If authorization requirements were known...

If eligibility was accurate...

If the workflow did not require redundant entry...

Then the claim should have a much easier journey.

The real opportunity isn't merely:

Better denial management.

It is:

Fewer preventable denials.

Those are two very different strategies.

The Downstream Addiction

Healthcare has become addicted to downstream solutions.

The claim fails.

Fix it.

The patient can't get authorized.

Fix it.

The documentation is incomplete.

Fix it.

The payer denies it.

Appeal it.

The payment is wrong.

Reconcile it.

The data doesn't match.

Correct it.

This creates an endless loop:

Problem → Human intervention → Temporary fix → Repeat.

We call it workflow.

I call it organizational déjà vu.

Same problem.

Different Tuesday.

The Most Important Question in Revenue Cycle Management

Forget for a moment about collections.

Forget about A/R.

Forget about denial rates.

Ask:

Where did the problem first become inevitable?

That question is radically different.

A denial may happen today.

But the conditions that produced the denial may have existed days or weeks earlier.

The authorization may have been incomplete.

The documentation may have been ambiguous.

The information may have been entered incorrectly.

The system may have lacked the necessary context.

The wrong person may have had to interpret the information.

By the time the denial reaches the billing department, the original cause may be ancient history.

Yet the biller gets the blame.

That's like blaming the fire department for the fire.

The New KPI: Preventable Work

Healthcare has plenty of metrics.

We need another category.

Preventable work.

How much of the organization's labor exists because something earlier went wrong?

Measure:

Rework.

How many transactions require correction?

Manual touches.

How many people must intervene?

Duplicate entry.

How many times is the same information typed again?

Clarification.

How often must employees contact each other because information is incomplete?

Exception handling.

How often does the transaction leave the normal process?

Preventable denial.

How many denials could reasonably have been prevented earlier?

Administrative time.

How many staff hours are consumed by correcting predictable problems?

Now we are measuring something meaningful.

The 30-Day Experiment

A clinic does not need to buy another platform tomorrow.

Try this first.

For 30 days, ask your staff to record every recurring administrative problem.

Not the dramatic ones.

The boring ones.

Especially the boring ones.

Because the boring problems are often the expensive ones.

Track five things.

1. Rework

How many times did someone correct information?

2. Re-entry

How many times did someone type the same information into another system?

3. Clarification

How often did one person need to contact another because information was incomplete?

4. Exceptions

How many transactions required a workaround?

5. Preventable downstream events

How many denials, delays or corrections could have been avoided upstream?

At the end of the month, don't immediately buy software.

Look for the pattern.

The pattern is your product specification.

Follow the Information, Not the Org Chart

This is one of the simplest exercises a clinic can perform.

Pick one transaction.

A new patient.

A procedure.

A referral.

A prior authorization.

A claim.

Now follow the information from beginning to end.

Don't follow the organizational chart.

Follow the data.

Where does it originate?

Who touches it?

Who changes it?

Who interprets it?

Who copies it?

Who approves it?

Who enters it again?

Who waits for it?

Who fixes it?

And where does it first become ambiguous?

That's where the real problem lives.

Handoffs Are Where Information Goes to Get Complicated

Consider a typical healthcare transaction.

Physician.

Medical assistant.

Front desk.

Authorization team.

Payer.

Billing.

Clearinghouse.

Payer again.

Payment.

Appeal.

That's a lot of hands.

Every handoff is an opportunity for information to degrade.

A field can disappear.

A date can change.

A diagnosis can be interpreted differently.

A requirement can be misunderstood.

A document can sit in someone's inbox.

And then somebody says:

“Why didn't billing catch this?”

Why did billing have to catch it?

That's the more interesting question.

AI Should Not Make Humans Faster at Doing the Wrong Thing

This is perhaps my biggest concern with the current AI enthusiasm.

We are measuring automation.

But we should measure human work eliminated.

Those are not the same thing.

Suppose an AI system reduces claim-review time from ten minutes to two.

Great.

But why is somebody reviewing the claim?

Suppose AI cuts authorization processing from 30 minutes to five.

Great.

But why did the authorization require so much manual interpretation?

Suppose AI cuts denial appeals from 20 minutes to three.

Excellent.

But why did the claim become a denial?

The first question celebrates efficiency.

The second question challenges architecture.

Healthcare needs both.

But we have been overinvesting in the first.

Simplify First. Automate Second.

This should be a law of healthcare technology.

Simplify first. Automate second.

Otherwise, you risk automating a bad process.

Then everybody becomes dependent on the bad process.

Then the organization spends millions maintaining the bad process.

Then nobody can change it because:

“That's how the system works.”

This is how technical debt becomes operational culture.

What Should AI Actually Do?

AI can:

detect patterns,
identify anomalies,
extract information,
classify documentation,
predict risk,
identify missing information,
surface inconsistencies,
assist with decisions,
automate repetitive work,
and help humans focus on higher-value tasks.

But I would give AI a more demanding mission.

Not:

Make humans faster.

Instead:

Make unnecessary human work disappear.

That's a higher standard.

It forces us to distinguish between:

work that requires judgment

and

work that exists because the system is poorly designed.

A physician's judgment is valuable.

Typing the same patient information into three systems is not.

A biller's expertise is valuable.

Hunting through five portals for information that should already be available is not.

A nurse's clinical assessment is valuable.

Calling three departments to find a missing authorization is not.

Human work should be expensive.

We should spend it accordingly.

What the Physician Organizations Just Told the AI Industry

This week's joint statement from six major physician organizations is important for precisely this reason.

The AMA, AAFP, AAP, ACOG, ACP and ACS pushed back against the idea that AI should be treated as inherently superior to physicians.

They emphasized context.

Experience.

Professional judgment.

Patient relationships.

And humanity.

Their message was not anti-AI.

Quite the opposite.

They explicitly acknowledged AI's tremendous potential.

But they made the hierarchy clear:

Technology should enhance medicine.

It should not diminish the physician.

And it should not undermine the patient's trust.

That is an important distinction for healthcare founders.

The goal isn't to make the human less important.

The goal is to make unnecessary human work less important.

Maralee's Story Makes That Principle Real

Think about the people in Maralee's story.

Maralee Lellio.

Andrew Lellio.

Ayla Lellio.

Rosa Lellio.

Halle Moore, MD.

These aren't workflow objects.

They aren't records.

They aren't encounters.

They aren't claims.

They are people.

Maralee wanted to live.

Andrew wanted his wife.

Ayla needed her mother.

Rosa entered a world where her mother was still there.

Dr. Moore treated a person whose life extended far beyond the clinical encounter.

That is what healthcare is supposed to protect.

The administrative infrastructure matters.

But infrastructure is not the mission.

The patient is the mission.

A Better Definition of Healthcare Efficiency

We have traditionally defined efficiency as:

More output with fewer resources.

That definition is inadequate for healthcare.

A physician seeing 30 patients instead of 20 isn't automatically more efficient if every patient gets less attention.

A billing department processing 10,000 claims instead of 7,000 isn't automatically more efficient if denials increase.

A call center answering more calls isn't automatically more efficient if patients have to call three times.

A portal that receives more messages isn't necessarily a success if patients use it because they can't reach anyone.

Healthcare needs a different definition.

More human value with less unnecessary work.

That is the metric I want.

The Human Attention Return

Imagine every healthcare technology company had to report one new metric:

Human Attention Returned

How many hours did the product return to:

Physicians?

Nurses?

Medical assistants?

Billers?

Practice managers?

Patients?

Caregivers?

Now the conversation changes.

A billing system that saves 1,000 hours matters.

But what happens to those hours?

If they simply create 1,000 more administrative tasks, nothing meaningful changed.

If those hours become patient-facing time?

Now we have something.

If physicians go home earlier?

Something changed.

If staff stop working Saturday mornings?

Something changed.

If patients get callbacks sooner?

Something changed.

If clinicians have more time to think?

Something changed.

The Real ROI of Healthcare Technology

We talk about ROI as dollars.

We should.

But healthcare technology has another return.

Return on attention.

What does the organization get back?

Time.

Focus.

Judgment.

Presence.

Patience.

Human connection.

These are harder to put into a spreadsheet.

They may be more important than what fits in the spreadsheet.

Three Questions for Every AI Vendor

Before buying another AI platform, ask:

What work disappears?

Not what the software automates.

What work actually disappears?

What work does your product create?

Every system creates some new work.

Be honest about it.

What happens when the AI is wrong?

This is perhaps the most important question.

In healthcare, failure is not merely a technical issue.

It can become a clinical, financial, legal or ethical issue.

Five More Questions I Would Ask

Where does your system intervene?

Upstream?

Midstream?

Downstream?

How does it improve data quality?

Or does it simply interpret poor data later?

How many human touches remain?

Don't tell me the system is “90% automated.”

Show me the remaining 10%.

That's where the interesting problems usually live.

Can we audit what happened?

If the answer is no, keep asking.

Does the technology prevent exceptions?

Or does it process exceptions faster?

Those are very different products.

The OnnX Perspective

The philosophy behind OnnX is intentionally different.

Medical billing has become an enormous downstream ecosystem.

Billing companies.

Clearinghouses.

Payer portals.

Coding systems.

Denial systems.

Authorization systems.

Analytics.

Work queues.

Dashboards.

Appeals.

And now AI agents.

The industry has become very good at moving information through an increasingly complicated maze.

But what if the answer isn't another better maze?

What if the answer is less maze?

OnnX is being built around the premise that much of the revenue-cycle problem begins upstream.

If the information is structured correctly at the point of capture, fewer downstream systems have to guess.

Fewer people have to interpret.

Fewer transactions require correction.

Fewer exceptions need escalation.

The revenue cycle becomes more deterministic.

Less reactive.

Less dependent on heroics.

That is the goal.

Don't just automate the mess. Prevent the mess.

This Isn't About Eliminating Billers

This distinction matters.

Prevention-oriented technology should not be confused with simply eliminating jobs.

Healthcare needs knowledgeable people.

Billing expertise matters.

Coding expertise matters.

Practice-management expertise matters.

The question is whether highly skilled people should spend their day doing highly skilled work.

Or whether they should spend it correcting predictable system failures.

Those are not equivalent.

A great biller should be solving difficult revenue-cycle problems.

Not searching three portals because an address was entered incorrectly.

A great practice manager should be improving the practice.

Not becoming the organization's human error-correction engine.

The Jobs That AI Should Replace

Let's be provocative.

AI should replace some work.

Absolutely.

But let's be specific.

AI should replace:

Copying.

Re-keying.

Sorting.

Searching.

Matching.

Flagging.

Routing.

Repetitive verification.

Predictable reconciliation.

Administrative detective work.

The objective isn't:

Replace the human.

The objective is:

Replace the unnecessary task.

That's a much better AI strategy.

The Future May Belong to Boring AI

Here's another prediction.

Some of the most valuable healthcare AI in the next decade will be incredibly boring.

No humanoid robot.

No dramatic demo.

No giant screen.

No science-fiction interface.

Maybe:

The claim never needed correction.

Maybe:

The authorization was automatically complete.

Maybe:

The physician never had to re-document the same information.

Maybe:

The biller never saw the transaction because nothing went wrong.

Maybe:

The patient never knew the software existed.

That's boring.

And beautiful.

Because the best infrastructure is often invisible.

The Difference Between Automation and Prevention

Automation asks:

Can a machine do this task?

Prevention asks:

Why does this task exist?

Automation asks:

Can AI process this exception?

Prevention asks:

Why did the exception happen?

Automation asks:

Can we make the denial workflow faster?

Prevention asks:

Can we make the denial less likely?

Automation asks:

Can we reduce clicks?

Prevention asks:

Why are there so many clicks?

That is the difference.

The 30-Day Challenge for Physicians and Clinic Owners

Try this.

For the next 30 days, don't ask your staff:

“How productive were you?”

Ask:

“What unnecessary work did the system create for you today?”

Write down every answer.

At the end of the month, rank the problems.

Then choose one.

Don't automate it.

Prevent it.

If you can't prevent it, simplify it.

If you can't simplify it, automate it.

If you can't automate it, at least make the responsibility clear.

That sequence matters.

What Should Clinics Measure?

A modern practice should consider tracking:

First-pass claim rate

How many claims succeed without intervention?

Manual touch rate

How many human interventions occur per transaction?

Rework rate

How many transactions require correction?

Duplicate-entry rate

How often is information entered more than once?

Exception rate

How often does a transaction leave the standard workflow?

Preventable denial rate

How many denials were reasonably foreseeable?

Authorization cycle time

How long does the process take?

Administrative hours per provider

How much physician and staff time is being consumed?

Patient-facing hours

How much time actually reaches the patient?

And one more:

Human attention returned.

That is the metric I would put on the executive dashboard.

Legal Implications

The more healthcare relies on AI, the more important accountability becomes.

Healthcare organizations should consider:

HIPAA and privacy obligations.
Data security.
Business associate requirements.
Access controls.
Auditability.
Documentation.
Human oversight.
Payer-contract requirements.
Fraud and abuse concerns.
False Claims Act exposure.
State-specific requirements.
Clinical liability.
Vendor accountability.
Model governance.

The legal question isn't simply:

“Does the AI work?”

It is also:

“Can we explain what happened?”

Who made the decision?

What information was available?

What did the system recommend?

Who approved it?

What happened when the system was wrong?

Healthcare AI requires accountability architecture.

Not just model architecture.

Ethical Implications

The ethical question is even simpler.

What happens to the human when we optimize the system?

Does the physician gain time?

Does the patient gain access?

Does the nurse gain attention?

Does the biller gain meaningful work?

Or does the organization simply increase throughput?

Because efficiency can be morally neutral.

Efficiency can also be used to scale the wrong thing.

A faster bad process is still a bad process.

The Privacy Paradox

Healthcare is entering an era in which more systems want more data.

AI wants data.

Analytics wants data.

Payers want data.

Platforms want data.

Vendors want data.

But more data does not automatically mean better healthcare.

Sometimes it means more complexity.

The future should not simply be:

More data.

It should be:

Better structured information.

Information that is available at the right moment.

In the right context.

To the right person.

For the right decision.

That is harder.

And much more valuable.

The Patient Doesn't Care Which Department Owns the Problem

This is another truth healthcare sometimes forgets.

A patient doesn't care whether the problem belongs to:

Billing.

Scheduling.

Authorization.

Coding.

Eligibility.

IT.

The payer.

The clearinghouse.

The physician.

The hospital.

The patient experiences the problem.

Imagine a patient saying:

“I completely understand that the authorization issue belongs to a different department.”

Nobody says that.

They say:

“Can somebody please fix this?”

The patient doesn't see our organizational chart.

Why should our technology?

The Most Important Handoff Is the One You Eliminate

Healthcare spends enormous energy improving handoffs.

Better handoff tools.

Better workflows.

Better communication.

Better alerts.

Better coordination.

Good.

But sometimes the best handoff is:

No handoff.

If the information can flow automatically, don't make a person carry it.

If the system already knows, don't make someone ask.

If the data already exists, don't make someone re-enter it.

If a decision can be made safely upstream, don't create a downstream work queue.

The best workflow may be the workflow you remove.

Why This Matters to Physicians

Physicians entered medicine to care for patients.

That doesn't mean they hate technology.

It means they want technology to respect the reason they entered medicine.

The recent joint physician-organization statement on AI made this point clearly.

AI should augment physicians.

It should not replace professional judgment.

It should not undermine trust.

It should support the humanity of clinical practice.

That means healthcare AI companies have a responsibility.

Not just to build smarter systems.

But to build systems that understand where human judgment belongs.

And where it doesn't.

Why This Matters to Clinic Owners

Clinic owners have another problem.

Margins.

Staffing.

Payer pressure.

Administrative costs.

Recruitment.

Retention.

Denials.

A/R.

Compliance.

Technology costs.

They don't need another software vendor promising to “revolutionize the workflow.”

They need fewer fires.

And fewer reasons to hire someone to put out those fires.

The best technology investment may therefore be the one that quietly eliminates the recurring problem everyone has accepted as normal.

Why This Matters to Healthcare Founders

For founders, the temptation is obvious.

Find an inefficient workflow.

Add AI.

Sell efficiency.

But there is a better opportunity.

Find the source of the work.

Understand why it exists.

Find the first point where information becomes ambiguous.

Move intelligence upstream.

Prevent the downstream event.

That is harder.

It may also produce a much more defensible company.

Because you're not just replacing labor.

You're changing architecture.

Healthcare's Next Competitive Advantage

The next healthcare advantage may not be:

More automation.

It may be:

Less unnecessary work.

The organizations that figure this out will have advantages in:

physician retention,
staff retention,
operating margins,
patient experience,
revenue-cycle performance,
administrative efficiency,
and potentially clinical capacity.

Not because they bought more AI.

Because they created less noise.

Three Things I Would Stop Doing Tomorrow

Stop Celebrating Rework

Rework is not productivity.

It is evidence.

Every correction tells you something about the system that produced the error.

Stop Calling Every Problem a Workflow Problem

Sometimes the workflow is merely the messenger.

The actual problem is upstream information.

Stop Buying Automation Before Understanding the Work

Otherwise you may build a faster machine for producing the same problems.

And then you will spend five years explaining why the machine is “transformative.”

Three Things I Would Start Doing Tomorrow

Start Measuring Preventable Work

If you don't measure it, the organization will continue producing it.

Start Designing Information Before Designing Workflow

Bad information creates expensive workflows.

Start Treating Human Attention as a Strategic Asset

Because it is.

A Question for Every Healthcare CEO

If you could give every physician in your organization five additional hours per week, what would you want them to do?