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.
By Dr. Wael Khouli, MD, MBA | Co-Founder & Chief Medical Officer, Authsnap.
I want to be direct about something: the denial problem in U.S. healthcare is not complicated to understand. It is complicated to fix, but the basic dynamic is straightforward. Hospitals deliver medically appropriate care, submit claims, and then watch a meaningful portion of those claims get denied. Most of those denials are never challenged. Revenue disappears. And somewhere along the way, we collectively decided this was just how things work.
It isn’t. And we need to stop treating it like it is.
I spent years as a Chief Medical Officer and Medical Director of Case Management before co-founding Authsnap. In those roles, I sat in the middle of this problem every single day, watching clinicians re-justify care they had already delivered, watching revenue cycle teams triage which denials they had bandwidth to fight, and watching recoverable revenue get written off because the math on appealing a $400 claim didn’t make sense when it took three hours of staff time to pursue. I understood why those decisions were made. I also understood what they were costing us.
The Numbers Are Worth Sitting With
About 15% of all hospital claims are denied. Across the industry, that adds up to roughly $262 billion in unpaid claims annually. Hospitals then spend approximately $19.7 billion a year just on the administrative work of contesting those denials, chart review, documentation assembly, appeal drafting, and payer follow-up. And after all of that, a significant portion of denied revenue is still never recovered.
Here’s the part that gets me: around 70% of appealed claims can be successfully overturned. The denials aren’t mostly legitimate. Many of them are wrong. The care was appropriate. The documentation was there. But the appeal never got filed, because the team didn’t have time, or the filing window closed, or someone made a judgment call that the claim wasn’t worth the effort.
That gap between what could be recovered and what actually gets recovered is not a clinical failure. It is a workflow failure. A capacity failure. And, increasingly, it is a technology failure.
Why This Has Gotten So Much Harder
When I started in hospital administration, denials were manageable as exceptions. You had a team, you had a process, and while it wasn’t elegant, you could keep up. That is genuinely no longer true for most health systems, and I think it’s worth being honest about why.
Payer policies are not just more complex; they change more frequently, with less notice, and with greater specificity than they did even five years ago. Medicare Advantage, which now covers more than half of all Medicare beneficiaries, has brought a new layer of utilization management scrutiny that many organizations are still trying to get their arms around. Prior authorization requirements have expanded into services that were once routinely approved. And the documentation bar for medical necessity has been raised in ways that put real pressure on already-stretched clinical teams.
At the same time, the workforce capable of managing this complexity is shrinking. It takes years to develop a skilled appeals specialist. Turnover in revenue cycle roles is high. And the cognitive demand of the work is not trivial — effective appeals require reading clinical documentation carefully, interpreting payer-specific criteria, and building a structured, evidence-based argument. That is genuinely hard. The quality of an appeal written at the end of a long Friday looks nothing like one written fresh on Monday morning.
Meanwhile, most hospitals are navigating all of this with fragmented technology, manually toggling between EHR systems, PDFs, and payer portals that were not designed to talk to each other. The tools haven’t kept up with the complexity.
What’s Actually at Stake
I want to make the downstream consequences concrete, because I think they get abstracted away in conversations about revenue cycle.
When a hospital consistently loses revenue it legitimately earned, the effects are real and visible. Capital investment gets deferred. Services get reduced or eliminated. Staffing decisions get made under financial duress rather than based on patient need. The American Hospital Association reports that 149 hospitals have closed in the past decade due to financial pressures. Those closures have consequences for communities, particularly in rural and underserved areas where access is already limited.
There is also a direct toll on the clinical workforce. Clinicians who spend hours re-documenting care they already delivered, re-justifying decisions they already made, dealing with prior auth delays for treatments their patients need now, that friction accumulates. It is a real contributor to burnout, and it pulls physicians and nurses away from what they actually came to do.
And patients bear costs too. Delayed authorizations mean delayed care. Denied claims generate confusing bills and unexpected financial exposure. For patients managing serious illness, administrative uncertainty on top of clinical uncertainty is its own burden.
The Shift That’s Starting to Happen
The health systems making real progress on this problem have made one fundamental reframe: they stopped treating denial management as a staffing problem and started treating it as a data and workflow problem. That reframe matters because it changes what solutions are even on the table.
If you think the problem is that you don’t have enough appeals specialists, your solution is to hire more appeals specialists. And you will perpetually lose that race, because the volume and complexity of denials is growing faster than any organization can staff against it.
If you think the problem is that the work is too manual, too inconsistent, and too dependent on individual capacity at any given moment, you start looking at how to systematize it. You look at where AI can take on the extractive, repetitive labor, ingesting clinical records, mapping documentation against payer criteria, generating structured appeal arguments, so that the clinical expertise in your organization can be applied to judgment, oversight, and the cases that genuinely require human reasoning.
The goal is not to remove clinicians from the process. Appropriate clinical judgment matters enormously in appeals, and bad claims shouldn’t be appealed just because automation makes it easy to do so. The goal is to stop asking skilled professionals to do work that does not require their skill, and to stop writing off legitimate revenue because the team didn’t have capacity on a given Tuesday.
What Forward-Looking Organizations Are Doing Differently
The health systems getting this right share a few things in common that go beyond technology adoption.
They treat denial data as operational intelligence, not just a report. They track denial patterns by payer, by service line, by physician, by reason code, and they use that data to drive prevention upstream, before claims are submitted. A denial that never happens is better than a denial that gets appealed and overturned.
They’ve built feedback loops between revenue cycle and clinical operations. When documentation gaps are causing denials, the people generating the documentation need to know — specifically, consistently, and without it feeling like a compliance audit. That connection between clinical and administrative functions is often missing, and it is expensive when it is.
And they have stopped accepting inconsistency as inevitable. The quality of denial management should not vary based on who is available, how tired they are, or whether it’s the end of the month. Consistency requires process, and increasingly, it requires tools that enforce that process at scale.
A Final Word
None of this is any easy. Changing how denial management works inside a health system requires organizational will, cross-functional coordination, and a willingness to challenge assumptions that have been baked in for a long time. I have been in those rooms. I know how hard those conversations are.
But I also know what it costs to leave this problem unaddressed. The revenue is real. The operational burden is real. The impact on staff and patients is real. And the tools to do better exist right now.
We owe it to our institutions, our clinicians, and our patients to stop treating $262 billion in denied claims as an unavoidable cost of doing business, and start treating it as a solvable problem.
By April Miller, Senior Technology Writer, ReHack.
Care delivery has become less about providing clinical attention and more about coordination through healthcare automation. Hospitals need to move patients through intake, diagnosis, treatment, discharge and follow-up. Additionally, they must manage pressures on staffing, documentation, payer requirements and growing data volumes.
When viewed as infrastructure for workflow, AI can reformat otherwise disjointed operational data into actionable signals for executives.
Why AI Belongs in the Workflow Conversation
Healthcare workflow automation refers to eliminating time-consuming steps in processing. This could include multiple entries of the same data, several steps in which patient information is passed from one recipient to another, and delays in providing reports to others. In January 2025, more states had implemented automated feeds of near-real-time hospital bed capacity. The CDC noted that automated data exchange is saving time and costs.
Such automation is important because clinicians and administrators can only optimize what they can see. AI-enabled tools can analyze historical scheduling data, bed availability, staffing, patient acuity and discharge impediments to identify pinch points. With a click, people can access indicators such as days when patient flow slows. Leaders can act sooner to allocate resources, coordinate transportation or influence discharges.
The strongest use cases remove friction from clinical judgment. AI can identify work queues to prioritize, flag missing documentation, surface relevant patient history and recommend next best actions. Health IT teams can deploy these systems broadly within the existing EHR and communication workflows.
Where AI Improves Efficiency and Safety
During intake and triage, generative and ambient models can help route patients based on symptoms, risk factors and service availability. They can also draft notes for the clinician to review, preventing after-hours charting.
AI may also ease workflows for diagnosis or care management, such as identifying abnormalities on imaging, predicting readmission risk or identifying shortcomings upstream of preventive care. These systems perform better when they deliver explainable recommendations and involve clinicians at key points. Compared to a model that inundates a nurse with alerts, one that brings the patients most likely to need intervention to the forefront is helpful.
Central to this work is the exchange of data. Healthcare represents roughly 30% of the global volume of information, and hospitals will only see reliable workflow efficiency gains from AI once they have secure and scalable infrastructure. A high-capacity EHR exchange enables organizations to receive clinical summaries, referrals, event notifications and PHI from partners without manual processing.
How AI Can Create Savings Without Sacrificing Care
AI can save the most money safely by reducing waste. For example, predictive staffing models can make detailed predictions about shifts. Revenue cycle tools could flag coding, prior authorization or claim problems earlier in the process. Also, supply chain models could prevent costly stockouts and anticipate use.
If AI can help automatically identify patients who have discharge planning needs, such as transport, home health coordination or medication reconciliation, the hospital can address them sooner. Shorter delays may assist patient flow, reduce avoidable length of stay and create capacity to receive new patients.
Cost savings require successful implementation, and health systems should measure whether AI tools save work or shift the review workload. A tool is not optimized if it saves 10 minutes for one department but adds 20 minutes to another.
Governance Keeps Healthcare Automation Useful
The use of AI in care delivery would be improved by privacy, bias, security and accountability guardrails. Hospitals could evaluate model performance across populations and also set escalation paths and ownership for recommendations. EHR systems are responsible for 61.3% of diagnostic mistakes stemming from electronic health record systems, which can occur when teams become complacent.
Strong governance includes training staff. Clinicians need to know what an AI tool can do, what it cannot do and when to override it. IT teams should monitor drift, downtime and integration failures, and leaders should start with workflows that have obvious pain points, clear outcomes and limited risk.
Building a Smarter Care Delivery Model
AI works best to improve efficiency, safety and affordability of healthcare processes. It’s particularly helpful when it connects a physical flow of patients, data and decision-making in the real world and is combined with interoperability and governance and frontline input. After identifying bottlenecks, teams can measure burden before and after implementing solutions, testing iterations until they produce a workflow manageable for providers and patients.
There is a major transformation happening inside healthcare organizations across the country, and it is being led not just by technology, but by experienced operational leaders. Healthcare administrators are helping turn the promise of AI and integrated systems into measurable, patient-centered results by combining operational expertise with strategic execution.
The widespread adoption of electronic health records over the past decade was more than a technology milestone. It was a leadership challenge. Healthcare administrators managed complex implementations, aligned stakeholders across multi-site organizations, redesigned workflows, and helped modernize how patient information is captured and shared.
That experience created a generation of leaders who understand both the clinical environment and the systems supporting it. Leaders who know how to manage budgets, improve workflows, support adoption, and guide organizations through operational change without disrupting patient care. That foundation is critical as healthcare enters a new era of AI integration and advanced analytics.
Today’s healthcare leaders are addressing long-standing operational challenges such as fragmented systems, inefficient referral processes, and inconsistent communication across care teams. Strong administrators recognize these are not permanent barriers. They are operational problems that require operational solutions.
Across hospitals, clinics, and physician networks, healthcare administrators are overseeing system integrations, implementing intelligent workflow tools, and improving access to real-time operational data. These efforts help streamline documentation, improve reporting accuracy, support care coordination, and reduce administrative burdens on clinical staff.
The difference between a successful technology implementation and a failed one is rarely the software itself. More often, success depends on leadership, governance, and execution. Effective healthcare administrators establish clear implementation frameworks, define accountability early, standardize workflows, and invest in training that supports long-term adoption.
Equally important, they communicate effectively throughout the process. Strong leaders bring clinical teams, operational departments, and executive stakeholders together early to ensure alignment and long-term success. In complex healthcare environments, that level of coordination is essential.
The strongest healthcare administrators also understand that technology initiatives are ultimately about improving the patient experience. Reduced delays, stronger care coordination, better visibility into outcomes, and improved communication between providers all directly impact the quality of care patients receive.
Healthcare is entering one of its most operationally complex periods, but it is also one of its most promising. AI, integrated platforms, and advanced analytics are creating new opportunities to improve efficiency and strengthen care delivery. The organizations that succeed will be those led by administrators who understand how to implement these tools responsibly, strategically, and with patients at the center of every decision.
Melissa A. Corneal, MBA, is an Operational Program Manager specializing in healthcare operations, EHR implementation, and technology integration. She is a member of the American College of Healthcare Executives and the Project Management Institute.
Healthcare is built on patient engagement. But when that engagement breaks down, patients don’t get the care they need, and regional practices are hit with the dual loss of time and money.
Patient no-shows and late cancellations remain one of the biggest and most persistent revenue leaks for regional practices. Each year, no-shows cost U.S. healthcare providers roughly $150 billion. Regional practices that lack the scale of larger health systems are hit especially hard by this loss of money and efficiency.
But no-shows don’t have to be a consistent revenue drain. Regional practices are redefining the patient experience by adopting automated, two-way communication systems. These systems can boost patient engagement, save practices time and money, and ensure patients get the care they need.
Old Systems, New Expectations
No-shows often come down to unmet patient expectations. So much of our world has become seamless. Think about the last time you booked a flight or shopped for a product online. The process is easy and intuitive. Healthcare systems, however, have not kept pace with changing expectations.
We see this in a national no-show rate of 18%. Patients expect personalized, streamlined experiences, but healthcare communication systems don’t meet these expectations.
According to research, traditional healthcare communications fail patients in three important ways. First, patients feel overwhelmed by communications, yet they still often don’t get the specific information they need. Next, patients feel frustrated when they have to call in response to an appointment reminder. And finally, patients feel messages are not meant for them personally. The same research has shown long waiting times, seeing different doctors for appointments, transportation issues, and fear of hospitals are among the most common reasons for no-shows. Other reasons include scheduling conflicts and miscommunication with medical staff.
The bottom line is that healthcare is struggling to keep up with modern patient expectations. If your cellphone carrier can send personalized communications when you need them the most, why can’t your doctor?
This is why many regional practices are turning to automated communication systems to close the gap between patient expectations and experience.
Automate Engagement, Boost Appointment Adherence
We’ve seen the results when healthcare practices shift from one-way reminder calls and manual follow-ups to two-way, automated, multi-channel communications. When regional practices adopt an automated, proactive approach to patient communication, appointment adherence improves and no-show numbers drop, strengthening patient-provider connections. These communication systems tailor reminders to individual patients using their preferred language and communication channel.
The results are clear. Studies show that telephone reminders can reduce no-shows from 21% to 7%. Reminders are even more effective when they are personalized and delivered when and how individual patients need them the most.
Research has also shown how practices can enhance patient reminders by mixing up content and format, simplifying information, and including specifics such as clinic locations and contact information. Most importantly, the study recommends personalizing reminders to individuals and letting patients control the reminders.
AI-powered engagement systems can help regional practices reach patients in their preferred language using their preferred channel, whether that is a text, phone call, or email. Automated systems also let patients cancel, ask questions, or reschedule at their convenience, reducing the communication barriers between patient and practice.
Automated patient management systems are an efficient way for regional practices to give patients exactly what they need to keep their appointments. Reminders that include specific information about tests or procedures can help alleviate anxiety that cause no-shows. Data analysis can also help practices tailor messages with specialized information for patients who might need a greater level of engagement to keep appointments.
Patients aren’t the only ones benefiting from automated engagement. Staff no longer have to spend valuable time making reminder calls, leaving voicemails, or contacting patients after a no-show. They are free to focus on the more complex parts of their jobs best suited to their training and expertise.
Stronger Patient Engagement, Better Care for Everyone
Reducing no-shows creates a positive ripple effect across regional practices. When more patients keep their appointments, they get the care they need. When practices’ schedules are more stable, they can see more patients, increase efficiency, and save money too often lost to no-shows. Boosting patient engagement with timely, personalized reminders breaks down barriers between patients and practices, benefiting both.
Simply put, if a patient gets the information they need when and how they need it, if they can seamlessly confirm and reschedule, they are more likely to keep that appointment. And in healthcare, that is a win-win.
