The healthcare industry is in a constant state of evolution, and nowhere is this more evident than in revenue cycle management (RCM). As we navigate towards 2025, healthcare leaders and revenue cycle teams are diligently working to keep pace with the latest RCM trends. Why? Because the most recent advancements in RCM are crucial for helping healthcare teams maintain financial stability, accelerate revenue, reduce denials, and deliver high-quality patient care. Staying competitive means understanding what’s trending in RCM, especially since your competitors are likely already on top of it.
At its core, efficient revenue cycle management is paramount for financial success in healthcare. It's the process that oversees a patient’s care journey from start to finish, encompassing everything from registration and scheduling appointments to settling outstanding balances. However, navigating the intricate world of medical billing, coding, and claims processing can be a substantial administrative burden. This is where the concept of "foolproof" systems comes into play, aiming to simplify these complexities and mitigate the risk of errors.
Traditionally, RCM has been rife with manual processes and system complexities that open the door to errors. But what if we told you that achieving a truly "foolproof" RCM operation is not just a pipe dream? It's becoming a reality through the strategic integration of Artificial Intelligence (AI) automation. AI can transform cumbersome manual processes and disparate system levels into a cohesive, efficient, and precise RCM operation, enabling billing companies to scale with confidence and precision.
The Quest for "Foolproof" Systems: RCM's Efficiency Imperative
The ultimate goal for any healthcare billing company is clear: streamline processes for enhanced efficiency and a significant reduction in denials. This isn't just about saving time; it's about safeguarding financial health and ensuring consistent, reliable revenue flow.
The reality, however, often tells a different story. Healthcare organizations must grapple with vast amounts of data. The complexities of billing systems, whether distinguishing between account-level and claim-level updates, or the reliance on manual "quick audit processes" for charge entry, introduce vulnerabilities. Even with systems that pull data from schedules, "errors can still be made".
This is why top healthcare leaders and revenue cycle teams are increasingly embracing AI and automation. About 80% of healthcare executives are increasing spending on IT and software due to the rise of AI technologies, including generative AI. These powerful tools improve efficiency, optimize workflows, and minimize errors, especially in RCM areas like patient registration, eligibility verification, claims processing, denials management, and payment posting.
Consider Georgi Georgiev's earlier expressed desire for "the right systems" to manage processes for multiple clients. This vision perfectly aligns with the promise of AI-powered systems. They are not just about automating tasks; they are about building a resilient, adaptable framework that can handle the nuances and complexities of modern healthcare billing.
Unpacking Operational Challenges from the Podcast
Let’s dive into some of the pervasive operational challenges that continue to plague revenue cycles, as discussed in the podcast, and how they highlight the urgent need for smarter solutions.
Charge Entry Errors: One of the most basic, yet persistent, issues in RCM is charge entry errors. Manual charge entry can be a high-volume task where individuals get "in a zone" and prioritize speed, potentially overlooking mistakes. This can lead to issues like the same exact service being entered and submitted twice, or similar services being incorrectly submitted as duplicates due to errors in provider or service location information. A prime example given is two office visits on the same date for the same patient, unintentionally submitted with the same provider when it should have been different. Preventing these types of errors often requires a "quick audit process" when entering charges, which can slow down the process.
System Hierarchy Headaches: Billing systems often operate at different "hierarchies or levels," such as the patient account level and the claim level. This distinction is critical because updating information on the patient account doesn't always automatically update a specific claim. For instance, if a patient’s insurance is updated on a Monday, but charges from an encounter created on Friday or Sunday (before the update) are pulled in on Tuesday, the claim might still carry the old, incorrect insurance information. Understanding how your billing system operates at these different levels, not just for insurance but also for providers, is essential for resolving and preventing various denials.
Global Period Management: Surgical procedures often come with a "global period"—either zero, 10, or 90 days—during which certain follow-up services are considered part of the initial procedure and not separately payable. If a patient has a procedure on Monday with a global period and then comes back on Tuesday for another service, even if unrelated, it will likely hit an edit. Manually tracking these global periods across all patients and procedures is a significant challenge. The podcast emphasizes the need for processes to "alert the coder when services are performed for a patient that had a procedure with global days," suggesting pop-up notes or alerts on encounter forms or within the system.
Missing Information: Denials due to missing or incomplete information, often categorized under general codes like CARC-16, are a constant source of frustration. These can range from missing primary payment adjudication information for secondary claims (N4 remark code) to missing authorization or referral forms (N489 remark code), or even a missing original claim number (ICN) when submitting a corrected claim (M47 remark code). Manually ensuring all required documentation and information is correctly transmitted with every claim, especially when dealing with electronic versus paper submissions, is a cumbersome and error-prone process.
AI as the Architect of Seamless RCM Operations
This is where AI, particularly agentic AI, steps in as the ultimate architect for truly seamless RCM operations. AI is revolutionizing healthcare, providing much-needed relief from the vast amounts of data healthcare organizations must contend with. Agentic AI, in particular, offers a unique solution because it can "autonomously perceives, decides, and acts to achieve its stated goals while adapting to new situations based on predefined instructions".
Let's look at how AI directly addresses the challenges we just discussed:
Intelligent Charge Capture & Validation: Instead of manual audits, AI can implement intelligent checks and validations before claims are submitted. Agentic AI can automate complex processes effortlessly, moving data between systems, navigating forms, and submitting information without human input. This drastically minimizes human errors in charge entry and reduces the risk of duplicate submissions, ensuring accuracy from the outset. Tools like Magical are making it easy for anyone to set up RPA workflows in a matter of minutes, not months.
Automated Data Synchronization: AI is perfectly positioned to bridge the gaps between different system levels. Agentic AI agents can be integrated with various systems involved in the revenue cycle, such as electronic health records (EHRs), billing systems, and payment gateways. They can "move and transform data between apps automatically," handling date conversions, text extraction, and formatting without manual cleanup. This ensures that when information is updated at the patient account level, it is seamlessly and accurately reflected at the claim level, preventing denials due to outdated or inconsistent data.
Proactive Workflow Alerts: While the sources mention the need for alerts for global periods and missing authorizations/referrals, AI elevates these to "proactive workflow alerts". AI-driven systems can automatically flag potential issues related to global periods, prior authorizations, and missing information. They can observe your team's workflows and automatically flag automation opportunities. Agentic AI's ability to make decisions just like a human, using reasoning models and real-time data, makes these alerts highly reliable and actionable, preventing denials before they occur.
Smart Coding & Bundling Edits: The podcast highlights the complexity of bundling denials and the need for expert coders and software to apply modifiers correctly or identify mutually exclusive services. AI can provide "smart coding & bundling edits". By leveraging machine learning algorithms, AI can learn from vast datasets, identify patterns, and make predictions, improving decision-making over time. This allows for automated analysis of coding combinations, ensuring compliance with coding guidelines and NCCI edits, preventing common bundling denials, and reducing compliance issues.
The Future of RCM: Scalability and Precision with AI
Embracing AI in RCM isn’t just about fixing current problems; it’s about paving the way for sustainable growth and operational excellence.
One of the most significant benefits is reducing manual burden and reallocating staff to higher-value tasks. Agentic AI can handle complex, repetitive tasks, freeing human workers to focus on strategic and creative endeavors that require critical thinking and empathy. This means your skilled RCM teams can dedicate their time to complex denial appeals, patient engagement, or strategic financial planning, rather than routine data entry or follow-ups.
Furthermore, AI ensures consistency and accuracy across all RCM functions. By automating tasks and applying rules consistently, AI minimizes the human error factor inherent in manual processes. This leads to more accurate claims, fewer denials, and faster reimbursements. Agentic AI agents are designed to adapt to changes and handle edge cases automatically, ensuring your automations keep running reliably. They even come with features like daily automated testing, detailed automation logs, and adaptive intelligence that adjusts if a button changes in an app.
Ultimately, this empowers billing companies to achieve sustainable growth through operational excellence. With AI handling the heavy lifting and ensuring precision, organizations can scale their operations without proportionally increasing headcount. This leads to increased efficiency, enhanced customer experiences (by providing personalized and responsive support), and improved decision-making through data analysis. The shift from a quantity-driven to a quality-driven model, putting patient outcomes at the forefront, also benefits patients.
Ready to explore how agentic AI can transform your RCM operations and help you build a truly foolproof system? Book a demo with Magical today and discover how our AI employees can automate your most complex workflows, reduce errors, and free up your team to focus on what matters most. Magical is loved by over 950,000 users and trusted by 100,000 companies, integrating with over 100,000 sites, making it a reliable solution for healthcare companies looking to automate complex RCM workflows.
A Closer Look: How AI Prevents Common Denials
Let's get even more specific about how AI directly tackles the most common denial culprits discussed in the podcast.
Duplicate Denials (CARC-18, CARC-B13): Duplicate denials, characterized by codes like CARC-18 ("exact duplicate claim or service") and CARC-B13 ("previously paid"), are often frustrating because they frequently stem from payer errors or internal resubmission issues, not true duplicates. The podcast highlights that prevention often involves confirming claim submissions before resubmitting and ensuring correct claim indicators are used for corrected claims.
"New denials can sink a profit margin, and given the cost of appeals, which is roughly $118 per claim, not all denials can be reworked. Practice submitting 50 claims a day at an average reimbursement rate of $200 per claim should bring in $10,000 in daily revenue. But if 10% of those claims are denied, and the practice can only appeal one, they lose $800 per day. That's upwards of $200,000 annually."
This statement underscores the massive financial drain of denials. AI automation can act as a crucial preventative layer here. For instance, an AI system can automatically check for prior claim submissions or processing before allowing a resubmission, preventing accidental duplicate claim batch submissions or individual claim resubmissions without changes. For corrected claims, AI can ensure the appropriate "resubmitting code seven" and original ICN are always included, reducing denials where the payer doesn't recognize the correction. Furthermore, AI can monitor for payer-side errors that lead to duplicate denials, flagging them for immediate reprocessing without manual intervention.
Eligibility Denials (CARC 26, 27, 31, 32, 33, 34, 200): Eligibility denials are often the number one cause of denials, yet they are highly preventable. Causes include incorrect insurance information, unconfirmed eligibility, or incorrect information on the claim itself (due to system hierarchy issues). The resolution often involves verifying eligibility online, checking both patient account and claim levels, and confirming all demographics and ID information.
"I tell you this so that you will seriously consider processes for preventing claims, as opposed to processes and resources committed to attempting to recover the revenue after the claim has been denied."
This emphasizes the preventative mindset. AI shines here. Agentic AI can automate the entire eligibility verification process before a patient is seen, pulling information directly from payer portals and ensuring the service date is correct. It can then automatically update both patient account and claim-level information, eliminating discrepancies caused by manual data entry or system hierarchy issues. For common issues like newborn enrollment delays or lapsed coverage due to missed premiums, AI systems can automatically flag these, providing real-time alerts to staff to initiate calls to the payer or guide the patient on next steps, rather than waiting for a denial.
Non-Covered Charges (CARC-96): Non-covered denials occur when services are not covered based on payer policy or patient benefits. This can be due to not following clinical policy guidelines (e.g., requiring a certain number of therapy sessions beforehand) or an exclusion in the patient's plan. Resolution often involves researching remark codes, checking LCDs (Local Coverage Determinations), or reviewing payer's clinical policy guidelines.
While clinical policy adherence often requires human decision-making from providers and coders, AI can significantly assist. AI can integrate with vast databases of payer guidelines and LCDs, proactively flagging potential non-covered services before they are rendered or billed. For instance, AI could alert staff if a procedure typically requires prior therapy sessions that are not documented. It can also help streamline the process of checking patient benefits by automating inquiries, allowing staff to communicate responsibility to patients upfront for PR (patient responsibility) denials, while identifying CO (contractual obligation) denials that cannot be billed to the patient.
Bundling Denials (CARC-97, 231, 236): Bundling denials happen when services billed together are considered inclusive of each other per coding or NCCI (National Correct Coding Initiative) guidelines, or if services are performed within the global period of a previously performed procedure. Preventing these often requires sophisticated coding software and careful tracking of global periods.
This is another area where AI excels. AI-powered coding software can automatically determine if multiple procedures are billed accurately with or without modifiers (like 25, 59, 24, or 79) by instantly cross-referencing against NCCI edits and coding guidelines. This "smart coding & bundling edits" capability helps prevent errors before submission. For global period management, AI can maintain comprehensive patient histories, automatically identify when a service falls within a global period, and flag it for review by a coder, ensuring appropriate modifiers are applied or charges are adjusted off if not supported.
Missing Information (CARC-16): CARC-16 is a generalized denial, but often accompanied by remark codes indicating missing primary EOBs for secondary claims (N4), missing referral/authorization forms (N489), or missing original ICNs for corrected claims (M47).
AI streamlines the transmission of required information. For secondary claims, AI can automate the process of capturing and transmitting primary payment adjudication information electronically, reducing the need for manual EOB entry. For authorizations and referrals, AI can ensure the correct information is transmitted with the initial claim and, if a denial still occurs, automatically check if it was truly transmitted before advising contacting the payer. For corrected claims, AI can implement edits within billing systems to ensure that if a resubmission code 7 is used, the original ICN is always included, preventing denials for missing control numbers.
Automate Your RCM Workflows with Agentic AI
The healthcare industry is constantly evolving, and staying on top of RCM trends is crucial for financial stability and delivering quality patient care. By embracing AI and automation, healthcare organizations can improve efficiency, optimize workflows, and minimize errors, especially in key RCM areas.
The transition from quantity-driven to quality-driven healthcare models, with patient outcomes at the forefront, makes the accuracy and efficiency provided by AI even more vital. AI-driven RCM solutions help healthcare providers reduce administrative costs, accelerate cash flow, minimize claim denials, and enhance patient satisfaction.
You can put these RCM trends into action by using Magical to automate your revenue cycle workflows today. Magical offers fully autonomous, end-to-end automation driven by AI employees that can problem-solve so automations don't break or fail. It's perfect for automating tasks like prior authorizations, claims management, and payment posting. Magical doesn't store keystrokes or patient data, ensuring zero risk of data breaches, making it a secure solution for sensitive healthcare information.
By taking a proactive approach and investing in innovation, revenue cycle leaders can guide their organizations through challenging times, ensure financial well-being, and help patients navigate their financial responsibilities with greater ease and clarity.
Ready to experience the magic of self-driving workflows? Book a free demo to learn how Magical can work with your systems and transform your RCM operations.
