By Suhas Nair, Executive Director of Product Management, AGS Health.
For the past several years, the conversation around AI in medical coding has been dominated by a single question: How much of the coding process can actually be automated? It’s an understandable question. Health systems continue to face mounting pressure from workforce shortages, increasingly complex coding requirements, evolving payer expectations, and persistent financial challenges. AI has demonstrated that it can help address many of these issues, particularly by accelerating routine coding tasks and improving consistency.
But in my conversations with healthcare leaders across the country, I have noticed a shift in the conversation. The organizations making the most meaningful progress aren’t asking how to remove people from the process. Instead, they’re asking how to combine AI with the expertise of experienced coding professionals to improve accuracy, strengthen compliance, and create more resilient coding operations.
That’s an important distinction.
Medical coding has never been simply about assigning codes. It requires interpreting complex clinical documentation, applying evolving coding guidance, understanding payer expectations, and exercising judgment when documentation is unclear. Those responsibilities don’t disappear as AI becomes more capable; if anything, they become more important.
This is why I believe the future of coding is less about autonomous AI and more about orchestrating human and AI collaboration: thoughtfully combining the speed and scalability of automation with the experience and judgment that only skilled coding professionals can provide. That is the model that matters most.
Why the Conversation Is Changing
That shift isn’t simply changing how coding gets done. It’s also changing how healthcare organizations think about AI’s role.
The timing of this shift isn’t accidental. Coding has become significantly more complex over the past decade. Documentation requirements continue to expand. Regulatory expectations evolve. Payer scrutiny has intensified. At the same time, organizations are expected to code accurately, submit claims more quickly, improve financial performance, and do it all with limited staffing resources.
It’s precisely this confluence of pressures that has made AI such an attractive investment. It also revealed that automation delivers its greatest value when it’s designed to complement human expertise rather than operate independently.
That realization is reshaping how many healthcare organizations approach coding modernization.
Early AI initiatives often focused on whether technology could replicate the work of experienced coders. Today, the conversation is more nuanced. Healthcare leaders are recognizing that while AI excels at processing large volumes of information quickly and consistently, coding still depends on clinical context, regulatory interpretation, and professional judgment.
That isn’t a limitation of AI. It’s simply the nature of healthcare.
The strongest coding operations have always combined technology, expertise, and well-designed processes. AI doesn’t change that equation. It strengthens it.
What Successful AI Adoption Looks Like
One lesson has emerged consistently as healthcare organizations expand their use of AI in medical coding: technology alone rarely changes outcomes. Instead, the organizations seeing meaningful operational improvements are redesigning workflows alongside the technology, allowing automation and human expertise to complement one another rather than compete.
While implementation strategies vary, several common characteristics appear among organizations that are moving beyond pilot projects and realizing measurable gains.
One is intelligent case routing. Rather than treating every encounter the same, organizations are increasingly matching work to the resource best equipped to handle it. Straightforward, high-confidence cases can often move through automated workflows, while encounters involving complex documentation, multiple comorbidities, or nuanced payer requirements are escalated to experienced coding professionals. The objective is not to maximize automation for its own sake, but to apply it where it creates the greatest value.
Another is the recognition that AI improves through collaboration. Human review doesn’t simply identify errors. It also provides the feedback needed to refine models as documentation patterns, coding guidance, and reimbursement policies evolve. In that sense, experienced coders remain central to the system’s long-term performance, even as automation assumes a greater share of routine work.
Transparency is also becoming more important as AI-generated coding recommendations play a larger role in the revenue cycle. HIM leaders, compliance teams, and auditors need confidence that recommendations can be understood, validated, and traced back to supporting documentation. Explainability is no longer just a technical consideration. It’s becoming an operational requirement.
Finally, organizations are taking a broader view of success. Productivity metrics remain important, but they tell only part of the story. Coding quality, audit outcomes, denial prevention, reimbursement accuracy, turnaround times, and workforce satisfaction all contribute to determining whether an AI strategy is delivering meaningful value.
Viewed together, these patterns suggest that successful coding automation isn’t measured solely by how much work AI performs independently. Instead, it’s measured by how effectively technology and human expertise work together.
The Medical Coder’s Role Is Becoming More Strategic
Perhaps the biggest misconception surrounding coding automation is that it’s primarily a technology initiative. In practice, success typically has less to do with algorithms and more to do with workflow design, governance, and how organizations choose to leverage the expertise already on their teams.
As AI assumes more routine coding responsibilities, the role of experienced coding professionals naturally evolves. Their focus increasingly shifts toward resolving complex cases, auditing AI-generated recommendations, supporting quality initiatives, and providing the oversight needed to maintain coding integrity as regulatory requirements continue to change.
This shift also presents an opportunity to rethink how coding expertise is used. Rather than asking highly skilled professionals to spend much of their time on repetitive, lower-complexity work, organizations can redirect that expertise toward the situations where clinical judgment has the greatest impact. In an environment where experienced coders remain difficult to recruit and retain, making better use of existing talent may prove just as valuable as increasing productivity.
The Future Is Digital Workforce Orchestration
For many organizations, the challenge is no longer deciding whether AI belongs in the coding workflow. Instead, it is determining how people and technology can work together to strengthen both operational performance and coding quality.
From where I sit, that’s where the conversation becomes the most interesting. Healthcare has never been an industry that embraces technology simply because it’s new. It adopts technology when it solves real problems, fits within complex clinical and operational environments, and earns the trust of the people using it every day.
The conversation around medical coding is maturing beyond replacement and toward collaboration. Instead of asking whether AI can do the work of a coder, healthcare organizations are asking where automation creates the greatest value and where human judgment remains indispensable.
That distinction changes everything.
Rather than viewing AI as a substitute for expertise, organizations are beginning to see it as a force multiplier. It enables coding professionals to focus on cases that require the highest level of clinical interpretation while allowing automation to improve efficiency, consistency, and scalability where appropriate.
I believe that’s where the greatest opportunity lies.
The future of medical coding won’t be defined by how autonomous AI becomes. It will be defined by how thoughtfully healthcare organizations combine technology with human expertise to create coding operations that are more accurate, more resilient, and better prepared for what comes next.
By Rob Ware, senior vice president and general manager of RCM Services, ModMed.
The financial health of medical practices is under strain. Historically, revenue cycle management (RCM) has been treated as a back-office administrative function, but today’s environment of rising operational costs, staffing shortages, and reimbursements that fail to keep pace with inflation demands a new approach.
Adding to the pressure, the industry remains plagued by considerable inefficiency. One study found that insurers, offering qualified health plans through HealthCare.gov, denied 19% of in-network claims and 37% of out-of-network claims. In this situation, administrators spend valuable time going back and forth with insurance companies while patients get hit with surprise bills. Financial stress goes up, institutional trust goes down, and the entire RCM process gets more complicated.
Prevention is the New Strategy
For decades, healthcare organizations have operated under a reactive RCM model. A claim is submitted, a denial occurs weeks later, and staff scramble to identify the problem, correct it, and resubmit the claim. The process has become an expensive cycle of administrative catch-up that drains resources and delays reimbursement.
But healthcare can no longer afford to manage revenue after the fact.
Predictive RCM represents a shift from revenue recovery to revenue prevention. By leveraging historical data and AI-powered insights, practices can identify potential issus before a claim is submitted.
Think of it as a navigation system for the billing workflow. Rather than notifying a user that they missed a left turn after they are already lost, predictive technology highlights potential roadblocks, such as missing authorization requirements, eligibility issues, or coding inconsistencies, while a claim is still being prepared.
The result is fewer preventable denials, faster reimbursement, and less time spent fixing avoidable mistakes.
Empowering the Back Office, Supporting the Front
One of the most important shifts occurring in healthcare today is strengthening the connection between clinical and financial workflows and teams.
Historically, those functions have operated in separate worlds. Yet many reimbursement challenges originate long before a claim reaches the billing department. Missing documentation, incomplete patient information, authorization gaps, and coding discrepancies can often be detected and addressed at the top of the patient journey.
Predictive RCM can help close this gap by identifying claims at risk of denial earlier and creating greater alignment between front-office, clinical, and billing teams. Rather than treating denials as isolated billing problems, providers can address root causes before they impact reimbursement.
This proactive approach not only aims to improve financial performance but also to reduce administrative friction across the practice.
When technology handles highly repetitive and predictable tasks—such as monitoring claim status, identifying missing information, or validating payer requirements—it allows revenue cycle professionals to focus on higher-value work, including complex appeals, payer negotiations, and strategic financial planning.
The benefits extend beyond the back office.
When front-office teams have access to accurate eligibility verification, timely coverage information, and reliable patient cost estimates, they are better equipped to have clear and compassionate financial conversations with patients. Instead of uncertainty, patients receive greater transparency and a clearer understanding of their financial responsibilities before care is delivered.
By shifting from reactive firefighting to predictive prevention, practices can potentially avoid costly denials while giving staff valuable time back.
The Human Impact
And then there’s the patient. Few experiences create more frustration for patients than receiving an unexpected bill months after an appointment.
As patients assume greater responsibility for healthcare costs, financial transparency is becoming an increasingly important part of the overall care experience. Providing accurate information upfront helps reduce anxiety, improve trust, and strengthen the provider-patient relationship.
When patients understand their coverage, expected costs, and payment options before treatment, practices are better positioned to create a more positive financial experience while reducing confusion and collections challenges later.
Ultimately, predictive RCM is about more than preventing denials. It represents a broader shift in how healthcare organizations think about financial operations.
In the years ahead, recovering revenue quickly will be only one piece of the financial success puzzle. Providers will be able to prevent revenue leakage before it occurs, align clinical and financial workflows more effectively, and create a more transparent experience for both staff and patients.
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
Every few months, another health system announces it has deployed artificial intelligence across its revenue cycle. The press release follows a familiar script: reduced denials, fastero authorizations, staff hours reclaimed, efficiency unlocked. What almost never appears in that announcement is a second document, the one that defines how the organization will know, 12 months from now, whether any of that is actually true.
That absence is not an accident. It reflects something deeper about how healthcare has historically treated its administrative infrastructure: as a problem to manage rather than a system to understand. And now, as AI tools move from pilot programs into operational deployment at scale, that gap is now creating real operational risk as AI moves into live production environments.
I have spent more than twelve years working alongside revenue cycle teams, coders, billers, authorization specialists, and CFOs, and I can say with some confidence that most of the people closest to this work are deeply skeptical of headlines. They have seen technology promises before. They remember the EHR implementations that were supposed to streamline documentation and instead added hours to the physician workday. They remember the clearinghouse upgrades that reduced one bottleneck and created three others downstream. They are not cynics. They are people who have learned, through experience, that what a system claims to do and what it actually does inside a live operational environment are often very different things.
That skepticism is not resistance to change. It is exactly the kind of operational discipline that should shape how AI gets evaluated and deployed.
The challenge right now is that the industry has skipped that step. Conference stages are crowded with transformation narratives. Health systems facing tight margins and persistent staffing shortages feel genuine urgency to find operational relief. All of that is understandable. But urgency without accountability is how you end up automating broken processes rather than fixing them. And in the revenue cycle, broken processes do not just affect the balance sheet. They affect whether a patient gets a procedure approved on time. They affect whether a physician burns another hour on paperwork that should have taken ten minutes. They affect the trust that providers, payers, and patients depend on to make the system function.
What I find missing in most AI deployment conversations is a straightforward commitment to answering a basic question before the contract is signed: what does success look like, and how will we measure it independently? Through clean, pre-specified performance benchmarks, first-pass resolution rates, authorization turnaround times, denial overturn rates, measured against a documented baseline and evaluated at regular intervals by people inside the organization who are empowered to say when something is not working.
Part of the reason is structural. Revenue cycle operations in most health systems sit in a complicated organizational space, accountable to finance, connected to clinical operations, dependent on technology infrastructure managed by IT, and constrained by payer relationships that nobody controls entirely. That diffusion of accountability makes it genuinely difficult to assign ownership over AI performance. When a denial rate creeps up six months after an AI tool goes live, the question of who is responsible for diagnosing why, whether the technology team, the RCM leadership, or the vendor, rarely has a clean answer. So the question often goes unasked, or gets absorbed into the background noise of operational management.
The other part is cultural. Healthcare administration has a long tradition of accepting complexity as inherent rather than examining it as designed. Prior authorization, to take the most visible example, has become so procedurally dense that many organizations have simply built workforces around navigating it rather than questioning whether the navigation itself can be fundamentally restructured.
The scale of that problem is not abstract: according to CMS, more than 53 million prior authorization requests were submitted to Medicare Advantage insurers in 2024 alone, and of the denials that were appealed, more than 80% were ultimately overturned. AI can reduce the friction of that navigation. But if the underlying logic of the process remains unchanged, if the criteria are still opaque, the payer responses still inconsistent, the documentation requirements still disconnected from clinical reality, then automation speeds up a broken system without healing it. That is a meaningful difference, and it is one that outcome measurement frameworks need to be designed to capture.
What better practice looks like, in my view, is fairly concrete. It starts with a pre-deployment audit with a clear-eyed inventory of where the revenue cycle is actually failing, not where it looks like it might benefit from technology. It requires that AI tools be evaluated against those specific failure points, with defined thresholds for what improvement looks like at thirty, ninety, and one hundred eighty days.
It demands that operational staff, the people who work inside these processes daily, have a formal mechanism to surface when a tool is creating new problems, not just solving old ones. And it insists that model performance be reviewed on a scheduled basis, because the payer landscape does not hold still, and a model trained on last year’s coverage criteria may be quietly degrading against this year’s.
None of this is technologically complicated. It is organizationally disciplined. And that distinction matters, because the conversations health systems need to have about AI accountability are not primarily conversations with vendors. They are internal conversations about how seriously the organization intends to govern its own operations.
Policymakers have a parallel responsibility. As federal and state attention increasingly focuses on prior authorization reform and payer transparency, there is an opportunity to embed outcome reporting requirements into any regulatory framework that governs automated administrative decision-making. An AI system that accelerates a payer’s denial process without improving clinical appropriateness is not a healthcare innovation. It is an efficiency tool for the payer, not an improvement in care decision-making. Regulators should require that distinction to be measurable and reported, not left to vendor interpretation.
The potential here is real. The revenue cycle absorbs an extraordinary share of healthcare resources, resources that could otherwise support direct patient care, workforce retention, or capital investment in underserved communities. Thoughtful AI deployment, governed by rigorous measurement, can free up meaningful capacity across the system. I have seen it work in contained, well-designed implementations. The problem is not that the technology cannot deliver. The problem is that without accountability frameworks, we will not actually know when it does, and we will not catch it when it does not.
Healthcare has spent years debating what AI can do. It is past time to build the infrastructure to find out what it is doing.
By Matthew Bernier, product management director and VP of PayerSync, Rectangle Health.
Healthcare providers are at a defining point, grappling with financial strain, often stemming from outdated and inefficient revenue cycle management (RCM) strategies.
These strategies, often riddled with manual inefficiencies and slow to adapt, are no longer sufficient to navigate the relentless tide of evolving payer regulations, skyrocketing denial rates, and the growing financial burden on patients.
RCM is widely recognized as an essential framework supporting the financial health and operational effectiveness of medical practices. Despite significant advancements in healthcare technology, many reimbursement processes remain outdated, cumbersome, and fragmented. This escalating pressure isn’t just a minor inconvenience; it’s actively eroding reimbursements, stifling cash flow, and ultimately compromising a provider’s ability to deliver essential patient care.
New research from American Express and PYMNTS revealed that 67% of healthcare payer executives reported that their firms’ reliance on manual payment systems is hampering their operational efficiency. Additionally, nearly 74% said that these outdated systems are increasing their exposure to regulatory fines and compliance penalties. Healthcare providers are already feeling the sting, meaning streamlining these processes is vital for redirecting valuable resources toward patient care and clinical services.
The Pitfalls of Outdated Reimbursement Methods
Many inefficiencies originate from continued reliance on traditional payment systems, notably paper checks and standard ACH transfers. While foundational in their own right, these payment methods were not designed to accommodate healthcare’s specialized requirements, such as the secure, compliant transmission of detailed patient remittance information. Providers frequently find themselves manually reconciling Explanation of Payments (EOPs) with deposits, a process prone to delays, errors, and unnecessary complexity.
Although ACH transfers represent a digital improvement over paper checks, standard ACH formats typically cannot include the comprehensive remittance details essential for precise and timely payment reconciliation. Additionally, financial institutions lack the infrastructure and incentives to manage HIPAA-sensitive information securely, adding administrative burdens and complexity for healthcare organizations.
Mounting Financial Pressures on Providers
The financial impact on providers due to outdated reimbursement methods is evident. According to a 2024 survey by Experian Health, 73% of healthcare administrators reported an increase in claim denials, rising from 42% just two years prior.
Several factors contribute to this decline:
Increased claim denials: Providers are seeing higher initial claim denial rates, especially from commercial health plans and Medicare Advantage plans.
Higher patient financial responsibility: The rise in high-deductible health plans and cost-sharing arrangements has placed more financial burden on patients, complicating the collection process.
Bad debt write-offs: Insured patients are increasingly accounting for significant amounts of bad debt, creating additional financial strain on healthcare providers.
Ongoing healthcare staffing shortages only amplify these challenges. Healthcare leaders report severe impacts from staff shortages, with 81% citing delays in care, longer wait times, and reduced access to essential services as significant issues. Providers, already stretched thin, are forced to divert limited resources to manage overdue payments, exacerbating administrative strain and creating uncertainty around cash flows and financial projections.
The Power of Next Generation Payment Rails
Addressing the persistent challenges of healthcare payments, next-generation digital payment rails offer providers a transformative path forward. Unlike standard ACH transfers, these advanced digital rails embed detailed remittance data directly within transactions, providing immediate, automated reconciliation. This integration reduces the time providers spend matching payments to claims, dramatically decreasing accounts receivable (A/R) days.
Providers already leveraging these innovative payment rails have experienced reimbursement processing times shrink from weeks to days. These streamlined systems automatically post reimbursements directly into practice management systems (PMSs) or electronic medical records (EMRs), eliminating manual data entry and reducing costly errors.
Next generation payment solutions meet patients’ evolving expectations. Modern online digital payment portals provide patients with transparent billing, cost estimates, flexible payment options, and insurance information. This is particularly important as nearly seven in 10 Gen Z patients report having payment issues with their latest healthcare service, highlighting a strong preference for convenient, contactless, and online payment methods. For providers, these solutions streamline billing through stored patient payment methods, deliver instant notifications, and offer consistent reporting across all payers, significantly enhancing financial visibility and control.
Digital Reimbursement: Accelerating Cash Flow and Accuracy
To overcome revenue cycle challenges effectively, providers should embrace automation and digitization within their reimbursement workflows. Modern, healthcare-specific digital reimbursement solutions securely integrate detailed, HIPAA-compliant patient data directly into financial transactions. This integration reduces manual reconciliation, enhancing accuracy and accelerating the reimbursement cycle.
Digitally automated reimbursement solutions consolidate various payment forms into a unified system, offering providers real-time transaction visibility and simplified reconciliation. By automating routine administrative tasks, healthcare staff can dedicate more time to high-value activities focused on patient care and practice growth, resulting in improved patient experiences and outcomes.
Additionally, automated reimbursement solutions provide immediate insights into payment statuses, equipping providers with accurate revenue forecasting, efficient budgeting, and proactive financial management.
The Path to Financial Strength
As reimbursement complexity grows, adopting automated and digitally integrated payment systems designed explicitly for healthcare becomes essential. Providers who modernize their reimbursement processes today will position themselves to handle industry challenges more effectively, securing their financial health, enhancing operational efficiency, and ensuring superior patient care for years to come.
AGS Health, a leading provider for tech-enabled revenue cycle management (RCM) solutions and strategic growth partner to healthcare providers across the U.S., announces the acquisition of the India-based patient access outsourcing business unit of the Florida-based healthcare technology company Availity.
With more than half of U.S. hospitals anticipating a year of negative margins, achieving full and accurate reimbursement for services has never been more critical. With this expansion, AGS Health is positioned to provide faster, more flexible financial clearance solutions at an even greater scale to help increase customers’ first-pass reimbursements rates.
“At AGS Health, our focus is greater financial freedom for healthcare organizations, allowing them to reinvest in their vision for superior patient care,” said Patrice Wolfe, CEO of AGS Health. “With this acquisition, we can better equip our customers with the tools and expert services necessary to help overcome payer complexities, reduce denial rates, mitigate revenue leakage, and accelerate cash flow on the back end.”
The acquisition allows AGS Health to expand the capabilities of the AGS AI Platform with new technology to enhance accuracy and scalability and further streamline patient access operations. The technology platform is capable of automatically determining, submitting, and verifying the status of prior authorization requests. Additionally, estimates of the patient’s out-of-pocket cost can be automatically generated based on payer rules set by the healthcare organization.
The move will also add approximately 200 patient access service team members to AGS Health’s global team of more than 11,000 college-educated, trained RCM experts. With labor shortages devastating all areas of the healthcare system, outsourced services are more critical than ever – particularly as financial clearance performance depends on access to qualified, skilled labor.
“This acquisition is just part of our continued investment in our customers’ success with automation technology and expert teams,” Wolfe adds. “As growth partners, we are committed to equipping our customers with flexible, modern solutions to mitigate risk and adapt to change. This is part of what we call ‘AI with a human touch’.”
By Brian Cafferty, vice president of RPA development, Tebra.
Robotic process automation (RPA) is a software technology that uses “bots” to replace repetitive, rule-based tasks and processes. In everyday life, people use RPA technology anytime they rely on form auto-fills, credit card payments, call center menus, or banking automation.
While the hyper-automation trend has taken root in most industries, bots are just starting to catch on in medical practices. The average practice performs many time-consuming and repetitive administrative tasks that require large amounts of data to be pulled, categorized, summarized, and reported. However, these tasks don’t require specialized knowledge. RPA can provide task automation across the organization, from front-office tasks to operational processes to patient interaction and bill payment. In addition, minimizing manual processes can reduce error rates that cost time and money.
Our customers and early adopters of RPA report that within the first three to six months of implementation, they saw improvements of 50% in operational efficiencies, 200% in claims processing speed, 30% in revenue per clinical encounter, 95% clean claims and only 2-3% claim denial rates.
Automating essential workflow
Onboarding new patients is a process you can automate while improving the patient experience. For example, a bot can provide an integrated data connection between your electronic health record (EHR) and billing platform. As patients complete the intake and insurance forms, this information is automatically entered into your billing platform.
As more patients demand convenient digital touchpoints, bots can maximize back-office efficiency with appointment scheduling. Bots can process patient appointment requests by presenting available time slots and adding selected appointments to the practice’s database. In addition, the RPA tools can assist with patient check-in, validating health plan coverage, and arranging pre-authorizations for planned procedures and treatments. For smaller practices or those with lean office staff, these automations can significantly reduce time spent on non-revenue-generating tasks.
For practices that need to migrate patient EHR data when transitioning from one practice management software to another, bots can initiate keystrokes to create a clinical note for each patient encounter to ensure the patient’s complete medical history is captured accurately.
By reducing the administrative tasks that do not require specialized knowledge or a human touchpoint, your practice staff can focus on delivering better care to improve patient outcomes and grow your practice.
By Joe McMurray, senior vice president of patient experience, Zotec Partners.
A July 2022 report confirmed what most providers have seen coming during this time of rampant inflation: Unexpected healthcare costs can be crippling for the majority of Americans. Many factors have influenced this fact, including rising high-deductible plans, ongoing pandemic stress, and the general truth that patients are often sick, scared, or confused — or a mix of all three. This strain poses many challenges for healthcare providers and their revenue cycle teams, highlighting the importance of patient-centric financial experiences.
Calculating cost estimates on unexpected medical encounters is a very challenging process, and if done so inaccurately, it can push patients to switch medical providers. According to PYMNTS, 46% of unwell patients have canceled an appointment because of high cost estimates, and two-fifths of patients who received inaccurate cost estimates spent more on healthcare than they could afford. Healthcare providers and organizations have seen drastic reductions in payment as a result.
Understanding Why Patients Don’t Pay
According to research by Debt.com, 45% of Americans have outstanding medical debt. Some reasons why patients can’t pay their debts includes financial hardship (which can be from job loss), murky healthcare billing systems, unexpected billings (especially during the holidays), and ambiguities with insurance. Inflation isn’t helping the situation, with almost 60% of people forgoing healthcare due to higher living expenses across the board.
Unpaid medical bills and their resulting medical debt are typically the outcomes of a combination of factors. First and foremost are unexpected healthcare costs, which is precisely what it sounds like: unplanned and unbudgeted medical expenses. The continued hike in high deductible plans and increased out-of-pocket expenses has also hit healthcare consumers’ wallets.
Additionally, uncertainty around billing is an issue for patients who need clarification on their responsibilities, billing due dates, or even which providers they saw during their encounters. Finally, technology can be a barrier to patient payments. When patients can’t access, understand, or act quickly on their bills, they are less likely to make a payment or pay in full.
Improving the Financial Experience for Patients
Health systems and clinicians shape patient care experiences, which can unfortunately lead to medical debt and devastating consequences in certain circumstances. So, what can healthcare providers do to alleviate these financial pressures for patients and set them up for success beyond diagnosis and treatment?
The first and most obvious response is to get the bill covered by the carrier prior to sending it to the patient. With advanced technology partners, this is a goal that should and can be explored. However, if there is still a patient portion, the following four steps will enhance the experience for all:
• Patient Education and Awareness
Healthcare organizations can help individuals make educated decisions about how to plan and pay for their care. Enhancing medical billing transparency means ensuring patients are aware of out-of-pocket expenses, including cost-of-care discussions in provider-patient interactions.
With the federal No Surprises Act in effect, patients now have increased transparency into what scheduled medical encounters cost. However, these estimates can only be accurate if no unplanned medical care or treatment is needed during service. By communicating up front with patients about additional costs, they will be more empowered when making healthcare decisions.
Once a patient receives a bill, it should be accurate, easy to understand, and convenient for them to take action.
• Payment Choices and Flexibility
Healthcare organizations can help patients with medical expenses by expanding, simplifying, and innovating payment options and plans. Offering more ways to pay based on patients’ preferences is essential, as is giving patients more time and flexibility. No two patients are alike, and based on their propensity to pay, providers can offer patients customized communications that offer payments through paper, phone, text, email, or portal access.
Offering payment plans is a proven way to increase collection rates. Patients who are offered additional time, even if it’s just a few weeks more, are more likely to make payments or pay their bills in full, reducing likelihood of medical debt. By adding a few more weeks to the billing cycle, providers can offer patients a more dignified and effective way to pay for services at a time most suitable for their financial situations.
• Compassionate Care Continuum
Healthcare expenses are a source of anxiety for many patients. Intimidating collection steps won’t do them any good, but a more compassionate billing approach could help increase patient payments.
Team members should utilize compassionate language as they guide patients through their journeys. When patients are confused, they should be met with a responsive contact center that leads with empathy and understanding. After all, calm patients feel more confident in their billing and are increasingly more vested in paying for the services rendered.
• Simple and Streamlined Technology
Providers should implement portals that make it easy for patients to pay bills, schedule appointments, review payment plans, and share feedback. Empowering patients with a self-service option enables greater transparency and customized experiences — all leading to higher payment capture.
By developing an extensive and dynamic patient journey by persona, organizations can customize communications by patient demographics and propensity-to-pay. This allows them to use the most innovative, intelligent means to request and receive payment. If providers don’t have a portal that meets these criteria, there are technology-enabled revenue cycle services partners that can further enhance the patient experience.
No two patients have the same pain points when it comes to medical expenses. And considering the economic landscape evolves daily, healthcare needs to be ready to adjust accordingly. Providers need to find flexible and intuitive ways to connect with patients and offer a variety of payment options to engage compassionately throughout the entire healthcare journey.
By Suhas Nair, Executive Director of Product Management, AGS Health.
