By Dr. Wael Khouli, MD, MBA | Co-Founder & CMO, Authsnap, Inc.
I just came off the stage at NPAC 2026 in Charlotte, where I presented to a room full of physician advisors on practical AI strategies for expanding bandwidth. The questions afterward told me everything I needed to know about where this profession stands right now.
Nobody asked whether AI was coming. They already know it is. They asked something more honest than that: how do we use it without losing the thing that makes us valuable in the first place?
That is the right question, and it deserves a real answer.
The Volume Problem Nobody Wants to Say Out Loud
Physician advisors are being asked to do more reviewing, more documentation, more peer-to-peer justification, and more appeals work than at any point in this profession’s history. Payer policies are more granular. Medicare Advantage utilization management has expanded into services that were once routinely approved. The documentation bar for medical necessity keeps rising.
The volume that now lands on a physician advisor’s desk has outpaced what any human can sustainably handle at the quality this work demands. That is not a criticism. It is arithmetic. And it is the honest starting point for any serious conversation about AI in this space.
The physician advisors I talk to are not afraid of AI replacing them. They are exhausted by a volume of work that was never supposed to be theirs, and they want to know whether AI can take some of it back.
There is a second category that gets even less attention: the work that never gets done at all. The cases that go unreviewed, the appeals that are never filed, the denials written off because there was no bandwidth to fight them. That work does not show up in a productivity report, but it is where revenue and patient access quietly leak out. With AI assistance, physician advisors and utilization management specialists can finally reach the work they could never get to before, not just move faster through the work already on the desk.
What AI Can Actually Do, and What It Cannot
Let me be specific, because vague claims about AI in healthcare help no one.
AI is genuinely good at the extractive, pattern-matching layer of physician advisor work: ingesting a clinical record and identifying the relevant diagnoses, treatment history, and documentation gaps; cross-referencing that information against payer-specific criteria; and generating a structured, evidence-based argument that a physician advisor can then review, refine, and sign off on. Done manually, that work takes one to two hours per case. AI brings it under ten minutes.
What AI cannot do is the clinical judgment underneath. Reading what a patient presentation actually means. Knowing when a payer’s stated criteria do not reflect the clinical reality of a complex case. Understanding the institutional context that shapes a particular denial pattern. Recognizing when the documentation tells a different story than the billing codes. That layer is not automatable, and any tool that claims otherwise is either wrong or selling something.
The physician advisors who will define this profession are the ones who understand exactly where that line sits. They use AI aggressively on one side of it and protect their judgment fiercely on the other.
The HIPAA Conversation Nobody Should Skip
I made this explicit at NPAC and I will make it explicit here: responsible AI adoption in physician advisory work requires HIPAA-compliant infrastructure, full stop. Not as an afterthought. Not as a future-state aspiration. Clinical documentation is protected health information, and any AI tool processing it needs to operate in a closed, compliant environment with audit trails and explainability built into the architecture.
The tools that earn physician advisor trust are the ones that make compliance visible, not the ones that ask you to trust a black box. When an organization deploys generic AI that lacks clinical specificity or payer fluency, the result is predictable: appeal quality drops, risk exposure rises, and the physician advisor ends up doing more cleanup than the manual process would have required. That is not a technology failure. It is a selection failure.
What Mastery Looks Like From Here
The physician advisors getting this right share a posture. They treat AI as a tool that earns trust through performance, not as a default answer to a capacity problem.
They verify AI-generated content before it goes out. They understand the clinical logic behind the output well enough to catch it when it is wrong. And they spend the time AI gives back on the work that requires judgment: the peer-to-peer conversations, the complex case reviews, the pattern recognition across a portfolio of denials that signals something systematic happening upstream.
AI bandwidth paired with physician judgment is the standard this profession is moving toward. The advisors who master it will handle case volumes that would have broken the old model. The ones who do not will fall further behind with every payer policy update.
The Honest Bottom Line
AI is not going to replace physician advisors. The clinical judgment, payer fluency, and institutional knowledge that make a great physician advisor valuable cannot be replicated by any model available today. But the administrative layer sitting on top of that judgment, the documentation extraction, the criteria cross-referencing, the initial appeal construction, can and should be automated.
That is not a threat to the profession. It is what gives the profession its future back. The physician advisors who embrace it will do the most important work of their careers. The ones who resist it will spend those same years buried in paperwork.
One caution belongs at the end of this. Moving forward, it will be difficult for physician advisors to stay at the forefront without becoming genuinely proficient at deploying AI in the right way and the right place. That proficiency runs in both directions. It means applying these tools where they create real leverage, and it means refusing to over-rely on them. The advisors who stop validating the output, who let AI quietly absorb the clinical judgment that only they can own, will not lead this profession. They will be exposed by it. Mastery is using AI aggressively while keeping a firm hand on the parts that must stay human.
By Ryan Dewey Smith, founding executive chairman & CEO, Inperium.
Behavioral health and human-centric services delivery is rapidly shifting to digital-first, A.I.-supported, and algorithmic modeling. Far beyond the realm of business functions like automating appointment reminders or facilitating insurance claim submissions, these technologies now are applied in clinical use to augment care.
A.I. holds promise for early medical intervention through its power to analyze full medical records and identify potential causation for patterns it recognizes, and clinicians in behavioral health services are increasingly utilizing digital therapeutic software to deliver evidence-based interventions to diagnose, treat, or prevent mental and behavioral health disorders.
From using A.I. language interpretation programs for clinicians to speak with diverse language populations or applying voice-to-text to generate timesaving summaries of patient sessions to employing A.I.-enabled wearables, there are numerous applications that hold real promise for better patient outcomes.
Like in every sector, emergent technologies are moving into widespread use at lightning pace, and like all emergent technologies, they are not cheap. A.I.-enabled devices share something with analog mobility and communication or sensory tools:
They are complex, difficult to maintain, and expensive. State-of-the-art technologies, whether used in the clinician’s back office or employed as an enhancement to medical care, follow the familiar pattern of being available only to those who have the financial wherewithal to afford them.
The harsh reality is that a large segment of those seeking professional assistance to manage mental health needs, addiction recovery, or disabilities services would never receive care if it were not for nonprofit providers. Our most vulnerable populations are overwhelmingly served by organizations already operating on razor-thin budgets.
Such providers lack the resources to access new technologies. In rural locations many providers have limited access to reliable broadband, an underlying requirement for implementing digital services. This fundamental digital divide is something they have in common with their impoverished patients, no matter their geographical location.
Yet a bridge across this divide could mean so much. Applications like an eye-tracking speech and visual output device or a smart medication dispenser that can track doses and send reminders are life-changing. We should be excited for the transformative innovations that are being developed, but the current structure threatens to worsen the existing disparity of access to them. Why should those with the greatest needs get left behind?
As the larger culture is carried on a tsunami into the A.I. future, we are reminded that we will require an equally sized tidal wave of education to use it effectively. As expensive as technology can be to purchase or use, that price pales compared to the human capital required to learn to use it well.
Successful adaptation will require an advanced set of critical thinking skills, immersive training in best A.I. practices, and heightened attention on data security measures to protect patient privacy. Not only will there be a learning curve, but there will also be vital data transition required, for in underfunded nonprofits, much of the treasure trove of data is stuck in analog systems (including paper record systems) and digital systems are often either outdated or cannot speak with one another. A.I. is useless with incomplete, inaccurate, or obsolete data.
The need for provider education is only half the equation. Reliance on patient usage of systems requires access to the technology and the know-how to use it. Many populations served lack one or both. Should we make the considerable investment required to realize all the benefits that these technologies can bring to the sector, it won’t have any meaning if we are not successful in providing the education our patients will require.
As with most difficult human problems, education can’t stop at “this is how it works;” it has to be adopted, which means investment in the time required to assist patients in altering their lifestyles.
People naturally fear the presence of “robots” in the parts of their lives that make them most vulnerable. Let’s learn the lessons of the COVID Pandemic, that a lack of human connection creates vast unintended consequences. Humancentric services can lead this transition by maintaining human connection while helping those they serve capture the benefits that nonhuman assets can provide.
Only human professionals can ensure that the most vulnerable among us are not made more vulnerable as we move into a technologically enhanced future.
Mealtime insulin is a routine part of diabetes care, but it also demands careful coordination among patients, prescribers, pharmacists, and caregivers. In that wider system, CanadianInsulin is a prescription referral platform; where required, it helps confirm prescription details with the prescriber, while dispensing and fulfilment are handled by licensed third-party pharmacies where permitted.
Some patients explore cash-pay options and cross-border fulfilment depending on eligibility and jurisdiction. Those system issues should remain separate from clinical decisions, which belong with a qualified healthcare professional who knows the patient’s diagnosis, glucose patterns, and treatment history.
Why Mealtime Insulin Pens Matter In Diabetes Care
Insulin pens are designed to help deliver measured doses in daily life. For many people, they are easier to carry and use than a vial and syringe. They may also support more consistent routines when meals, work, school, or travel make timing difficult.
Humalog KwikPen is one example of a prefilled insulin pen used for mealtime insulin. The important clinical point is not the pen alone. It is the combination of insulin type, dose instructions, meal timing, glucose monitoring, and follow-up care.
Rapid-acting insulin can lower blood glucose quickly. That makes it useful around meals, but it also means mistakes can have fast consequences. A missed meal, extra dose, unusual activity, or illness can change the risk of low blood sugar.
What Rapid-acting Insulin Lispro Does
Humalog is a brand name for insulin lispro. Insulin lispro is a rapid-acting insulin analog. It is made to start working faster than regular human insulin, so it is often used near meals or to correct high blood glucose when prescribed.
So, is Humalog KwikPen the same as insulin? More precisely, it is a disposable prefilled pen that contains insulin lispro. The medication is insulin lispro; the KwikPen is a delivery device. This distinction matters because patients may receive the same insulin in different formats, such as pens, cartridges, or vials.
What kind of insulin is HUMALOG 100? HUMALOG U-100 contains 100 units of insulin lispro per milliliter. U-100 is a concentration, not a dose. A prescribed dose still depends on the individual plan written by the clinician.
Rapid-acting insulin is commonly used with a longer-acting insulin in people who need basal and mealtime coverage. Some people with type 2 diabetes use mealtime insulin only after other treatments no longer provide enough control. Others, including many people with type 1 diabetes, require insulin as a central part of daily survival.
Where Pens Fit In tTreatment Decisions
A pen can simplify the mechanics of injection, but it does not decide the treatment plan. Clinicians consider age, diagnosis, kidney and liver function, eating patterns, glucose data, other medicines, vision, dexterity, and support at home.
Some patients need fixed mealtime doses. Others use insulin-to-carbohydrate ratios or correction scales. These approaches require education and periodic review. A plan that worked months ago may not fit after weight changes, new medicines, pregnancy, illness, or changes in activity.
Pens can be helpful for people who need discreet dosing outside the home. They may also reduce some measuring steps compared with syringes. Still, pen use requires training. Patients must understand how to attach needles, prime the pen if instructed, select the prescribed dose, inject correctly, and dispose of needles safely.
Device choice may also be affected by coverage rules, local availability, prescriber preference, and pharmacy substitution policies. These are administrative factors, but they can affect continuity of care. Patients should tell their care team before switching formats or concentrations.
Safety Checks that Reduce Avoidable Errors
The most important safety risk with rapid-acting insulin is hypoglycemia, or low blood sugar. Symptoms may include shakiness, sweating, fast heartbeat, confusion, hunger, weakness, or irritability. Severe hypoglycemia can cause seizures, loss of consciousness, or injury.
Patients should ask their clinician how to prevent, recognize, and treat low blood sugar. They should also know when to use emergency glucagon if it has been prescribed. Driving, exercise, alcohol, delayed meals, and overnight routines may need specific planning.
Dosing errors are another major concern. Insulin products can have similar names or packaging. Concentrations can differ. A U-100 product should not be confused with more concentrated insulin unless a clinician has clearly changed the prescription and provided education.
Several basic checks can reduce risk:
Confirm the insulin name and concentration before each new supply is used.
Use the pen only for the person it was prescribed for.
Do not share pens, even if a new needle is attached.
Rotate injection sites to reduce lumps or skin changes.
Use a new needle as instructed and dispose of it in a sharps container.
Follow storage instructions and ask a pharmacist about heat, freezing, or travel concerns.
Patients should report repeated lows, unexplained highs, injection-site problems, allergic symptoms, or confusion about technique. They should not adjust mealtime insulin on their own unless their care plan clearly explains how to do so.
Access Questions Belong In the Care Pathway
Access to insulin is not only a pharmacy issue. It is also a continuity-of-care issue. Delays, coverage changes, prior authorizations, travel, or prescription mismatches can interrupt treatment and raise clinical risk.
Patients can reduce disruption by keeping an updated medication list and knowing the exact insulin name, concentration, device type, and prescriber instructions. Caregivers should know where supplies are kept and what to do if doses are missed or blood glucose becomes unsafe.
When administrative questions arise, the prescriber’s office and pharmacy are usually the key sources of clarification. They can confirm whether a substitute is clinically appropriate, whether a prescription needs revision, and whether device training is required.
Patients should also ask how much insulin to keep on hand within the limits of their prescription and local rules. Planning is especially important before holidays, severe weather, travel, or changes in insurance status. The goal is not stockpiling; it is avoiding preventable gaps in a medication that may be time-sensitive.
Summary for Patients and Caregivers
Mealtime insulin pens sit at the intersection of clinical care and everyday logistics. A pen can make dosing more portable, but safe use depends on the correct insulin, clear instructions, glucose monitoring, and timely communication with the care team.
Humalog KwikPen contains insulin lispro, a rapid-acting insulin used around meals or as otherwise prescribed. HUMALOG U-100 refers to a 100 units per milliliter concentration. Patients should not treat concentration, device format, or brand name as interchangeable without professional guidance
This content is for informational purposes only and is not a substitute for professional medical advice. Anyone using insulin should follow the plan provided by their healthcare professional and seek urgent care for severe low blood sugar, serious allergic symptoms, or unsafe glucose readings.
By Errol Weiss, chief security officer, Health ISAC.
Hospitals have spent the last decade modernizing their digital infrastructure. But faced with various technical and regulatory hurdles, and sometimes resistance from employees, formal adoption of new tools has proved difficult.
According to a 2015 survey of healthcare institutions, only 15 percent of employees said their organization was “very ready” to adopt new technology, citing compliance as the primary hurdle.
Those very compliance challenges are now being exacerbated by AI, simply because employees want to use this new technology to do their jobs better and faster. AI adoption is often informal, and outpacing the speed at which most governance structures can move.
Clinicians and administrative staff are adopting AI-powered tools to streamline documentation, communicate with patients, and automate repetitive workflows. In many cases, these tools are introduced into the tech stack, or into a vendor’s systems, not through formal IT procurement processes but through well-meaning individual initiatives.
Overall, we’re seeing a widening gap in how health sector organizations want to use AI and how this powerful new technology is actually being used both inside and outside their walls.
An Unofficial AI Ecosystem
The rush to adopt AI makes sense, especially in healthcare, where clinicians are under pressure from staffing shortages and time constraints have only intensified; documentation alone can consume hours of a physician’s day. Against this backdrop, generative AI tools offer a practical shortcut: faster note-taking, detailed and deep summaries of patient histories, and lower administrative friction.
But because software approval processes can be slow, complex and risk-averse at health sector organizations, it’s not unusual to see some employees bypass them entirely. In practice, this creates a parallel ecosystem of “off-record” AI tools (or “shadow AI”) operating alongside sanctioned systems.
One survey found over 51% of healthcare organizations had relied on vendor disclosures to discover shadow AI usage – the unauthorized or unvetted use of AI tools. In other words, employees just tried different AI tools without approval, compliance, or standardization in mind.
But before we start blaming employees for negligence, we must recognize that this behavior is driven by urgency. Healthcare requires speed and efficiency, while governance processes prioritize long review cycles, vendor vetting and compliance checks to manage risk. The mismatch is becoming more pronounced as AI tools become easier to access and embed into everyday workflows.
Shadow AI is Risks Galore
Such rapid, informal adoption of AI tools introduces a range of risks that healthcare organizations are only beginning to grapple with.
The primary concern is data privacy. When a clinician types patient information into an unapproved AI chatbot to summarize a consultation or draft a treatment plan, they risk exposing the patient’s protected health information (PHI) to external systems. Depending on the tool, this data could be stored, retained, or used for model training in ways that violate internal policies or regulations.
This can lead to severe compliance violations. Healthcare organizations operating under HIPAA must ensure strict controls over how patient data is handled and processed. Shadow AI usage can inadvertently create compliance gaps that are difficult to detect in real time.
There are patient safety considerations, too. AI-generated outputs, particularly clinical summaries or suggested text, can contain inaccuracies or omissions simply due to the non-deterministic nature of AI models. If these outputs are incorporated into medical records without proper verification, they may introduce errors into clinical decisions around diagnoses or prescriptions.
Governance Must Evolve to Keep Up
Traditional IT governance structures were not designed for the speed or accessibility of modern AI tools. That’s especially true in healthcare, where regulatory requirements drive extensive validation, legal review, and security assessments for software and vendor approval processes. But these critical steps can take months – time a clinician may not spare when they have a ready AI tool to make their work easier.
Traditional governance frameworks struggle with the lack of categorization of AI tools. Traditional policies tend to distinguish between “approved” and “unapproved” software, but AI tools blur those lines. A single AI agent might function as a documentation assistant, a search tool, a patient history database, and a generative writing engine at the same time — a problem when the tool is approved for one use case and not another.
This creates blind spots in oversight. IT departments may approve the use of an AI platform at the enterprise level without full visibility into how embedded AI features are being activated at the user level.
Another challenge is the absence of consistent AI-specific governance standards tailored to healthcare workflows. Many existing frameworks focus on data security and vendor compliance, but do not fully account for risks unique to generative AI, such as hallucinated outputs, prompt sensitivity, or unintended disclosure of protected health information through user input.
This makes it difficult for security and governance teams to evaluate tools consistently, particularly as AI-enabled systems evolve rapidly.
Closing the Gap
Information security and resilience professionals must realize that the goal is not to block AI adoption, but to bring it under responsible governance frameworks that reflect how the technology is being used. That requires visibility. Organizations need better mechanisms to identify where AI tools are being used across clinical and administrative workflows.
Another focus is enabling safe and fast pathways to adoption. If hospital staff are turning to external tools because the internal systems are too slow or limited, organizations may need to reassess how the approved AI solutions are evaluated and deployed. This may mean you could shorten approval cycles for lower-risk use cases, and provide pre-vetted AI tools that meet security and compliance standards.
Education is also critical. Clinicians adopting AI tools may not fully understand the data handling implications of entering patient information into third-party AI tools. Clear guidance on what is and is not permissible can reduce unintentional risks.
A Shift is Underway
AI has outpaced nearly every other technology in how quickly it’s being adopted in the healthcare sector. So the challenge is not whether AI is used in clinical environments, but how quickly organizations can align governance, security, and operations to ensure the tools being used are the right ones.
Healthcare systems that succeed will not be the ones to implement AI in the most workflows. The winners in this race will be the organizations that make AI’s usage visible, secure, and sustainable.
Healthcare organizations operate in an increasingly complex financial environment where accuracy, speed and compliance directly impact profitability. As reimbursement models evolve and administrative burdens increase, hospitals and provider groups are turning to automation and artificial intelligence to optimize financial performance across the entire revenue cycle.
What Is Revenue Cycle Management?
Revenue cycle management is the end-to-end financial process healthcare providers use to track patient care, from initial appointment scheduling and registration to final payment collection. It includes multiple interconnected stages such as coding, billing, claims submission, payment posting and denial management.
Each stage is vulnerable to inefficiencies and manual errors that can disrupt cash flow, where even small inaccuracies in coding or eligibility verification can lead to claim rejections or payment delays. As such, challenges in this cycle can have a significant financial impact on healthcare organizations.
For example, according to the Centers for Medicare & Medicaid Services (CMS), the Medicare Fee-for-Service program alone recorded $28.83 billion in improper payments in fiscal year 2025, with an improper payment rate of 6.55%. These errors include documentation gaps, coding inaccuracies and billing mistakes, issues that originate directly within the early stages of the revenue cycle.
How Automation Impacts Revenue Cycle Management
Modern revenue cycle management automation is reshaping how healthcare organizations manage financial operations by embedding AI and machine learning into core workflows.
1. Streamlining Patient Registration and Eligibility Verification
The revenue cycle begins at registration, where inaccurate patient data can trigger downstream billing issues. As such, automation tools now validate insurance eligibility in real time, reducing manual verification work. AI-driven systems can also flag missing or inconsistent demographic information before claims are created, significantly reducing avoidable denials.
Denials are one of the most costly challenges in healthcare finance, so automation transforms denial management from a reactive to a proactive process. Machine learning models analyze historical denial patterns to identify root causes such as coding errors, eligibility issues or payer-specific rules.
These insights allow organizations to prevent future denials rather than simply correcting them after the fact. Denial management and prevention provide measurable improvements in turnaround times, patient financial clearance and self-service collections.
This proactive approach reflects a core theme from the 2026 AGS Health Summit, which identified front-end denial prevention, powered by a “hybrid intelligence model” of AI supporting skilled staff, as a primary driver of financial returns.
2. Enhancing Medical Coding Accuracy and Efficiency
Medical coding is a critical but complex and error-prone part of the revenue cycle management process. It involves translating clinical documentation into standardized codes used for billing and reimbursement, so even small gaps or interpretation errors can lead to claim denials, delays or compliance risks.
As such, automation is increasingly used to support this process, helping identify relevant clinical details within patient records and automate encoding. These tools help reduce manual workload while also improving speed, consistency and accuracy. A successful automation can save hours and possibly days of work. For example, a 45-second file transfer in an old method can take no more than a second with new workload automation software.
Additionally, AI algorithms trained on large billing datasets can identify discrepancies in submitted claims to detect potential fraud and recommend corrective actions, which enhances transparency and compliance.
3. Improving Billing and Claims Submission
Billing errors and incomplete claim submissions are major contributors to delayed reimbursement. As such, automation platforms streamline claims generation by validating payer rules before submission. This includes checking for missing modifiers, incorrect patient data and payer-specific formatting requirements.
In fact, there can be an increase in reimbursement accuracy by up to 25% with AI. By reducing the number of claim failures, healthcare organizations improve first-pass acceptance rates and shorten revenue cycles.
4. Supporting Decision-Making With AI
Beyond task automation, AI adds a layer of predictive intelligence to revenue cycle management operations. Analytics can forecast reimbursement timelines, estimate denial risks and identify revenue leakage points across departments. This allows finance and organizational leaders to make data-driven decisions that improve both operational efficiency and financial outcomes.
The Future of Revenue Cycle Management
Automation is fundamentally reshaping healthcare financial operations by streamlining workflows across the entire revenue cycle. From registration to denial management, intelligent systems reduce friction, improve accuracy and accelerate reimbursement.
As healthcare continues to shift toward value-based care and increased financial accountability, adopting advanced technologies in revenue cycle management will be essential for long-term sustainability and profitability
By Leigh Poland, RHIA, CCS, CDIP, CIC, is Vice President – Coding Services, Clinical Quality, and Education, AGS Health
The latest ICD-10 update may look insignificant to many healthcare organizations. There are no sweeping diagnosis code additions, no major guideline rewrites, and no dramatic restructuring of the classification system at first glance.
That perception could become a costly mistake.
The April 2026 ICD-10 changes introduced by the Centers for Medicare & Medicaid Services (CMS) and the National Center for Health Statistics (NCHS) are deceptively quiet. While the diagnosis code set itself remains largely untouched, the update alters something far more consequential: the decision-making framework coders use to determine sequencing, coexistence, and classification relationships. In practical terms, the update shifts more responsibility onto coder judgment, documentation precision, and organizational oversight.
For health systems already navigating staffing shortages, denials pressure, increasing payer scrutiny, and growing dependence on encoder technology, even modest classification logic changes can create operational instability.
The Real Change Is Not the Codes
The 2026 ICD-10-CM release includes no additions, deletions, or revisions to diagnosis codes. The Official Coding Guidelines also remain unchanged. But focusing only on code counts overlooks where the actual disruption is occurring.
The most meaningful changes involve instructional notes, exclusions, and indexing logic embedded within the classification system itself. These structural revisions alter how diagnoses relate to one another and how coders determine sequencing priorities.
Historically, ICD-10 relied heavily on embedded hierarchy through directives such as “code first” and “use additional code.” Those instructions created relatively rigid sequencing expectations. The April update softens several of those relationships by replacing them with “code also.”
That wording change appears minor. Operationally, it is not.
“Code also” removes automatic sequencing hierarchy and places greater emphasis on the clinical circumstances of the encounter. As a result, two experienced coders reviewing similar documentation may now reasonably arrive at different sequencing conclusions.
That variability introduces downstream risk for MS-DRG assignment, reimbursement consistency, quality reporting, and audit exposure.
Hypertensive Emergency Becomes a Judgment Call
One of the clearest examples appears in category I16.1 for hypertensive emergency.
Previous instructional language reinforced sequencing expectations around the hypertensive crisis itself. Under the revised structure, coders must now determine whether the hypertensive emergency or the associated complication represents the principal reason for admission.
In real-world inpatient settings, that distinction can materially alter reimbursement outcomes.
If the case emphasis shifts toward complications such as acute kidney injury, myocardial infarction, encephalopathy, heart failure, or cerebral infarction, the resulting DRG assignment may change significantly.
What was previously more standardized now becomes more interpretive.
For revenue integrity teams, this creates a new challenge: ensuring consistent organizational logic across coding staff, CDI specialists, and auditing functions.
Expanded Coding Combinations Increase Complexity
Another major change involves the conversion of multiple Excludes1 notes to Excludes2 notes. Within ICD-10 methodology, this distinction matters enormously.
Excludes1 notes prohibit reporting two conditions together because they are considered mutually exclusive. Excludes2 notes acknowledge that conditions may coexist when clinically appropriate.
The April revisions expand the number of valid diagnosis combinations across several clinical areas, including hematologic disorders, respiratory failure, and substance-related conditions.
That expansion creates both opportunity and risk.
On one hand, organizations may now capture clinical complexity more accurately. On the other, newly permissible combinations may attract increased payer attention if documentation does not clearly establish coexistence and medical necessity.
Respiratory failure coding illustrates the issue well.
The revision affecting postprocedural respiratory failure now allows certain respiratory failure conditions to be reported concurrently when documentation supports both diagnoses. Depending on sequencing and present-on-admission indicators, these changes can influence CC/MCC assignment and case severity calculations.
Increased flexibility sounds beneficial until organizations realize it also increases variation.
Technology Alone Will Not Solve This
Many organizations assume encoder systems will absorb these changes automatically. That assumption deserves caution. Encoder logic can support compliance, but it cannot fully resolve interpretive ambiguity introduced by structural classification changes. When sequencing hierarchy is loosened, technology becomes more dependent on human documentation quality and coder judgment.
This is particularly important as hospitals continue expanding the use of AI-assisted coding workflows.
Automation performs best in environments with stable and predictable rules. The more classification systems rely on nuance, contextual interpretation, and clinical prioritization, the more critical human oversight becomes.
The April ICD-10 update quietly reinforces that reality.
Healthcare organizations increasingly pursuing autonomous coding strategies may find that classification logic changes expose gaps in governance, validation, and audit readiness.
While the diagnosis side of the update focuses on logic restructuring, ICD-10-PCS continues expanding to capture emerging procedural complexity. New codes support advancements in cardiac pacing technologies, including conduction system pacing techniques involving ventricular septal lead placement.
Additional updates improve specificity for hepatobiliary and pancreatic drainage procedures by distinguishing transpapillary and transmural approaches commonly used in advanced endoscopy.
The update also expands reporting capabilities for reconstructive urologic procedures, rehabilitation therapies, electrotherapeutic modalities, and new technology interventions involving biologics, vascular scaffolds, gene therapies, and immunotherapies.
These additions reflect a continuing challenge for healthcare organizations: clinical innovation is moving faster than many operational infrastructures can adapt.
The significance of this update extends beyond HIM and coding operations. Sequencing variability influences reimbursement predictability. Documentation inconsistency affects denial vulnerability. Coding interpretation impacts publicly reported quality measures and risk adjustment performance.
In other words, structural coding logic changes eventually become enterprise financial and operational issues.
Organizations that dismiss this release because it lacks major code volume changes may underestimate its cumulative effect over time.
The healthcare industry often focuses attention on large regulatory overhauls while overlooking smaller classification refinements that quietly reshape operational behavior. This update falls squarely into that category.
The Organizations Most Likely to Struggle
The greatest risk may not come from the coding changes themselves but from uneven organizational response.
Health systems with mature auditing programs, strong CDI integration, and consistent coding governance will likely adapt relatively quickly.
Organizations with fragmented workflows, inconsistent education practices, or overreliance on automated coding recommendations may experience wider variability in coding outcomes.
The most immediate priorities should include:
Focused auditing of high-variability categories, such as hypertensive emergency and secondary glaucoma
Education around newly permissible diagnosis combinations
Validation of encoder and grouper functionality
Alignment between coding, CDI, compliance, and revenue integrity teams
Increased review of documentation sufficiency for concurrent condition reporting
The danger is not a dramatic overnight disruption. It is the gradual accumulation of inconsistencies across thousands of encounters.
A Quiet Update With Long-Term Consequences
The April 2026 ICD-10 revision is a reminder that healthcare reimbursement systems do not need sweeping reform to create operational consequences.
Sometimes the most impactful changes are the least visible.
By loosening embedded sequencing hierarchy, expanding allowable diagnosis relationships, and increasing procedural specificity, the update subtly changes how coding decisions are made across the enterprise.
That shift places greater pressure on judgment, governance, and the integrity of documentation at a time when healthcare organizations are already balancing financial strain and operational complexity. The organizations that recognize the significance early will be better positioned to maintain coding consistency, compliance stability, and reimbursement accuracy.
Those who treat this as a routine update may discover the real impact only after denials, audits, and DRG variation begin to surface.
By Ganesh Ramamoorthy, Senior Vice President, Onix.
Today’s digital healthcare professionals face unprecedented complexity. The quality and accessibility of clinical data is vital to delivering the best possible patient outcomes. Yet clinicians often struggle to quickly find and retrieve relevant information.
In fact, the sheer volume of data is staggering. A single hospital can produce 137 terabytes of data every day, or roughly 50 petabytes of data per year. This data tsunami is only getting worse, due to rapid expansion of digital health tools, electronic health records and connected devices.
As a result, healthcare administrative costs continue to skyrocket. In fact, administrative spending is estimated to be between 25 %-30% of the nearly $5 trillion spent annually for U.S. healthcare expenditures. More importantly, failure to tame the data dilemma can substantially impact both regulatory compliance as well as patient outcomes.
The healthcare industry is in dire need of transformation, but change happens slowly. How can healthcare providers navigate this massive, complex system to streamline data management in order to reduce costs, grow revenues and increase efficiencies?
Empower Intelligent Insights
To address this issue, a growing number of healthcare leaders are leveraging the latest artificial intelligence (AI) advancements to transform their legacy data systems into a modern, scalable and agile data platform. In this way, healthcare chief information officers (CIOs) are able to take full advantage of augmented intelligence to unlock predictive data analytics and clinical insights, enabling measurable improvements without adding administrative burden.
Indeed, AI-powered data modernization enables organizations to realize substantial clinical and operational benefits, while improving return on investment (ROI). With the help of enterprise-grade agentic AI and generative AI (Gen AI) technologies, healthcare organizations can achieve measurable results such as 10-25 percent reduction in the cost of care, 15%-20% drop in hospital readmissions, and substantial reduction in mortality rates.
Collaborative Compliance
It’s no secret that administrative friction in healthcare is a significant challenge, with nearly 25% of every dollar spent on paperwork. A primary driver of this cost is the prior authorization (PA) process, which typically requires a significant amount of time to conduct manual reviews, send faxes and make phone calls. This burden not only increases costs, but also delays patient care through a “missing information” loop, where simple administrative omissions trigger denials and appeals.
By leveraging the latest agentic and Gen AI, healthcare professionals can transform their workflow from “Reject and Appeal” to “Detect and Clarify” to greatly improve the speed, precision and outcomes of the PA process. The system works by ingesting unstructured clinical notes and matching them against insurance policies, enabling AI agents to perform a real-time gap analysis.
When information is missing, the AI agent flags the issue and drafts a clarification for the provider in less than a minute, ensuring valid claims are approved on the first pass. This not only streamlines billing, it also allows nurses and doctors to focus on patient outcomes rather than paperwork.
Privacy Protections
Of course, when dealing with sensitive patient data, it’s paramount that hospitals and healthcare organizations have access to reliable, secure data they can trust to ensure regulatory and HIPAA compliance. This means that a key aspect of selecting the best AI solution is to ensure it is an enterprise-grade offering that prioritizes a high level of security, data governance and compliance.
In fact, advanced AI capabilities enable additional privacy innovations as well. For example, with the help of GenAI, hospitals can generate millions of records of synthetic data, allowing them to train and test new AI models without exposing sensitive protected health information (PHI). Plus, by compressing processes that otherwise take hours or weeks into minutes, AI agents return valuable time to medical practitioners.
It’s important to note, however, that healthcare CIOs need to implement robust governance policies when taking advantage of AI technology. As the number of AI agents making autonomous decisions increases throughout the healthcare industry, responsible AI practices will become a mandatory business requirement with decisions being driven by trust and transparency.
Healthcare Transformation Success
Today’s healthcare industry is poised for progress, and responsible AI deployments will be an integral part of this transformation – from building a new level of personalized patient experiences, to realizing substantial gains in productivity for improved patient outcomes.
Armed with the right tools, intelligence and insights, healthcare leaders are empowered to realize this transformation and build a brighter future for their patients. The true differentiator for successful healthcare enterprises will not be if they use AI, but rather how they responsibly manage and fully integrate AI into established processes.
As healthcare becomes increasingly shaped by AI-driven discovery, online reputation has evolved from a marketing consideration into a core component of patient acquisition, retention, and trust. Today’s patients are not only evaluating providers based on clinical quality, but also on the digital signals that shape first impressions — from online reviews and search visibility to the consistency and authenticity of patient feedback across platforms.
At the same time, healthcare organizations are navigating a delicate balance: embracing AI to improve efficiency and patient insights while preserving the human connection that defines quality care. The practices succeeding in this environment are those using technology to remove friction, uncover actionable feedback, and strengthen patient relationships — not replace them.
In this Q&A, Evan Steele and Austin Colvard of rater8 discuss how healthcare organizations can prioritize patient experience, maintain Net Promoter Scores above 90, improve online discoverability in the AI era, and build reputations that accurately reflect the quality of care they deliver.
Evan Steele
Questions for Evan Steele, Founder + CEO, rater8:
How do you help practices ensure that their online reputation matches their quality of care?
The gap between care delivery and online perception is still one of the biggest inefficiencies in healthcare. Most practices are delivering excellent care, but only a fraction of that experience makes it online. We focus on systematically capturing patient feedback at scale and structuring it in a way that search engines and AI systems can actually understand. When you consistently collect verified reviews and publish them in the right places, your online reputation starts becoming a true reflection of reality.
Is the Net Promoter Score (NPS) the most universally trusted measure of practice quality?
NPS is a useful directional metric, but it’s incomplete on its own. It tells you how people feel, not necessarily why they feel that way. In healthcare, context matters. A 90+ NPS is meaningful, but only if you can tie it back to specific patient experiences and operational drivers. The practices that perform best don’t just track NPS; they pair it with qualitative feedback and real-time insights to understand what’s actually driving loyalty.
What challenges does the “AI era” pose for practices?
AI is fundamentally changing how patients are finding and choosing providers. We’re moving from a world of “ten blue links” to one where AI-generated results are giving a single answer. If your practice isn’t represented accurately in the data those systems rely on, you effectively disappear from consideration.
How can practices use AI to help improve their online reputation?
The biggest opportunity with AI is accessibility. Historically, extracting insights from patient feedback required time, tools, and expertise. AI removes that barrier. You can ask simple questions like “What are patients frustrated about in the last 30 days?” and get immediate answers. That allows practices to move faster, close gaps sooner, and continuously improve the experience they’re delivering, which ultimately drives stronger reviews and better online visibility.
How can practices maintain a human connection with their patients as AI increasingly helps connect patients and providers?
AI should handle the operational friction, not replace the human moments that matter. Patients don’t remember how efficiently they booked an appointment, they remember whether they felt heard, respected, and cared for. The goal is to use AI to create more space for those interactions, not less. The practices that get this right use technology to streamline the background work so their teams can be more present where it matters most.
Austin Colvard
Questions for Austin Colvard, VP of Professional Services, rater8:
How do online reviews impact practice success and discoverability?
Online reviews play a direct role in both patient decision-making and visibility. Patients are using them to validate whether a practice is worth their time, and search platforms are using them to decide which practices to show. If reviews aren’t coming in consistently, you lose ground in both areas. Recency and volume show that the experience is still consistent today, and the testimonials have the added benefit of boosting the confidence a patient has in booking an appointment. The combination of all three maximize discoverability.
What data are patients and prospective patients most concerned with when it comes to selecting or staying with a practice?
Patients are typically drawn to listings with a strong volume of reviews, while also paying close attention to any negative feedback. The listings with the most reviews help build a foundation of trust in a provider or practice by creating a baseline assessment of the quality of care. From there, seeing some negative reviews acts like a sanity check that often reassures the potential patient that they are making the right decision. For more specific care, such as procedures, having reviews that reference those procedures is especially important as well.
Most patients start with the star rating, but they don’t stop there. They’ll usually read through a handful of reviews to get a better sense of what the experience is actually like.
What tends to stand out are patterns. Things like whether the provider takes time to answer questions, how the staff interacts with patients, and how smooth the overall visit feels. Wait times and communication come up pretty frequently as well. It’s less about clinical detail and more about whether the experience feels consistent and trustworthy.
Should practices put more emphasis on Answer Engine Optimization (AEO) and GEO (Generative Engine Optimization) than Search Engine Optimization (SEO) in today’s AI landscape?
It’s less about shifting away from SEO and more about building on it. The same foundational pieces still matter: accurate information, strong review presence, and consistent content. What’s changing is how that information gets used. AI tools are pulling from those same sources, just presenting them differently. So the focus should really be on making sure your data is complete, consistent, and easy to interpret in every place patients and AI tools are searching, especially review sites that were historically considered secondary to Google, such as Healthgrades, WebMD, and Vitals.
What are the first steps practices can take to improve their online reputation?
The first step is making sure you’re asking every patient for feedback in a consistent way. A lot of practices are only capturing feedback from a small portion of their patient base, which can skew things over time and tremendously hinder building AEO/GEO/SEO authority at a competitive rate.
From there, double-check that all listings are accurate and up to date across platforms. After that, I’d focus on how reviews are being handled. Responding consistently, especially to negative feedback, goes a long way in showing that the practice is paying attention, willing to improve, and open to taking service recovery measures.
What’s the most common misconception you hear from practices about managing their online reputation?
The most common misconception is that online reputation is something that can be fixed quickly or treated as a one-time effort. In reality, it reflects what’s happening day to day in the practice and compounds over time. The teams that see the most improvement are the ones that build it into their routine. They’re consistently collecting feedback, reviewing it, and making small adjustments over time. In turn, they have the most visibility and success attracting and retaining patients.
