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Key takeaways: Manual work and disconnected claims management systems are often error-prone, resulting in delayed and denied claims. Technology, like automation and AI, can help healthcare organizations predict and prevent potential claims issues before submission. Implementing AI-powered claims management solutions should be a top priority for revenue cycle leaders. Healthcare claims denials are on the rise — but so is a new era of technology that can predict and prevent denials before they occur. Leveraging artificial intelligence (AI) for claims management can help organizations break the denial cycle and keep revenue cycles churning. In this article, we’ll explore how solutions like Experian Health’s innovative Patient Access Curator and AI Advantage™ are designed to help providers reduce claim denials with ​AI. Explore how Experian Health is reshaping the way health systems manage Coordination of Benefits. Learn how automation and AI are eliminating manual errors, reducing denials and unlocking millions in recoverable revenue. Watch now > Updating healthcare claims management tools  Claims management is one of the most pressing challenges in healthcare billing. In Experian Health’s 2024 State of Claims survey, 77% of providers said they were moderately to extremely concerned that payers won’t reimburse them, largely due to changing payer policies and prior authorization requirements. Revenue cycle leaders know that good claims management is the key to healthy cash flow and a strong financial foundation. However, with patient volumes growing and complex payer rules increasing, traditional claims management solutions can no longer keep up. As a result, today’s healthcare organizations are feeling the squeeze to update their claims management processes and adopt solutions that rely on automation and AI-powered analytics to better predict, prevent and process denials. Predicting and preventing denials with artificial intelligence​ Healthcare providers can stop the denial spiral before it begins by capturing accurate and complete patient data at registration. According to Experian Health data, 46% of denials are caused by missing or incorrect information. Now, many healthcare organizations are accelerating their digital transformations by implementing automation and AI tools designed to predict and prevent denials. Automation creates consistent workflows, standardizes routine tasks and reduces human errors. At the same time, AI takes claims management to the next level by predicting denials, flagging claims errors before submission and prioritizing claims that need attention. Leveraging AI solutions that form a closed-loop system can ensure clean data at registration while predicting and preventing denials. Front-end solutions Tools like Patient Access Curator automatically find and correct patient data within seconds — across eligibility, Coordination of Benefits (COB) primacy, Medicare Beneficiary Identifiers (MBI), demographics and insurance discovery. Machine learning and predictive analytics allow providers to identify and correct bad data in real time, without the need for guesswork. Ken Kubisty, VP of Revenue Cycle at Exact Sciences, shares how Patient Access Curator improved eligibility processes, reduced errors and more. Back-end solutions Experian Health’s AI Advantage uses AI and machine learning to predict and prevent denials. AI Advantage not only predicts claim outcomes mid-cycle, but pushes urgent tasks to the front of the queue — allowing staff to prioritize the claims that matter most financially. Extending the automation advantage To minimize denials and delays, providers can look to implement automation and artificial intelligence across the entire claims ecosystem. For instance, Patient Access Curator and AI Advantage integrate seamlessly with solutions that manage the entire claims cycle, like Experian Health’s ClaimSource® — using real-time insights generated by ClaimSource to detect patterns and predict future payer behavior. Additionally, tools like Claim Scrubber can automate the claim scrubbing process — reducing potential errors, administrative burden and the need for costly reworks. Organizations can also add a denials workflow manager to automate and optimize the denial management portion of the claims cycle, improve staff productivity and speed up reimbursement. Artificial intelligence for claims management ​FAQs Want to learn more about how Experian Health’s AI tools can help reduce and prevent claim denials? Consider these commonly asked questions. What is AI Advantage, and how does it help with healthcare claims management? AI Advantage works in two stages of claims management, with two offerings: Predictive Denials and Denial Triage. In stage one, Predictive Denials uses AI and machine learning to look for patterns in payer adjudications and identify undocumented rules that could result in new denials. This solution also flags claims with a high potential of denial, so the right specialist can intervene before claims go to payers. After a claim has been denied, AI Advantage’s stage two component uses advanced algorithms to identify and segment denials based on their potential value. What is Patient Access Curator, and how does it help reduce claim denials? Experian Health’s Patient Access Curator is a robust patient intake and verification solution designed to eliminate errors that often result in denials, such as missing or incorrect information. Through AI and robotic process automation, Patient Access Curator automatically checks and verifies patient demographic information, insurance details, eligibility and more — reducing claim denial rates and administrative burden. How can AI Advantage and Patient Access Curator work together? Patient Access Curator and AI Advantage form a closed-loop system that offers healthcare organizations a smarter, faster and more scalable way to reduce denials and increase reimbursements while reducing administrative burden on staff. What are real-world results from using these ​solutions? Case study: Experian Health and Exact Sciences See how Exact Sciences used Patient Access Curator to reduce denials by 50% and add $100 million to their bottom line in six months. Case study: Experian Health and Schneck Medical Center See how Schneck Medical Center used AI Advantage to achieve a 4.6% average monthly decrease in denials. The bottom line: Providers can reduce claim denials with AI Leveraging artificial intelligence for claims management can improve the overall efficiency and accuracy of healthcare claims processing — leading to fewer denials and a more seamless patient experience. Instead of waiting for denials to occur before taking remedial action, healthcare organizations can stay a step ahead with claims management solutions that utilize AI and automation. These tools can help proactively detect errors and diagnose claims process weaknesses for a healthier revenue cycle. As Jason Considine, President at Experian Health, recently shared: “With the power of AI and predictive intelligence, we’re no longer waiting for denials to happen; we’re helping providers proactively prevent them. Tools like Experian Health’s Patient Access Curator and AI Advantage allow healthcare organizations to identify issues at the point of registration and throughout the revenue cycle, so teams can focus on care, not corrections. It’s about working smarter, reducing risk and protecting ​​revenue.” Find out more about how Experian Health’s AI-powered claims management solutions help healthcare providers improve reimbursement rates and reduce denials. Learn more Contact us

Early diagnostics, remote patient monitoring and personalized care recommendations are just a few examples of how artificial intelligence (AI) is transforming the way healthcare is delivered. As technology advances, so do opportunities to optimize clinical and operational processes. With projected savings in the region of up to $360 billion annually, it's no surprise that 75% of healthcare executives believe AI has reached a turning point in their industry. Yet many providers are still just scratching the surface. Only a small percentage use AI for complex tasks like claim denial management, leaving the competitive advantage wide open. Understanding how these technologies work – and where to apply them for maximum impact – will be crucial to improve efficiency, remain competitive and above all, deliver excellent patient care.  The power of AI in healthcare As the name suggests, artificial intelligence refers to a machine's ability to perform cognitive tasks that would normally be associated with humans, such as problem-solving and decision-making. It can spot patterns, learn from experience and choose the right course of action to achieve a goal. Natural language processing, robotics and machine learning might all be in the mix. AI in the healthcare industry has been found to support applications like: Improving diagnosis through the analysis of medical images AI-powered wearables and virtual nursing assistants Patient data management Reducing and preventing insurance claim denials. Artificial intelligence in healthcare isn't a substitute for human contact, which underpins the best patient care. However, by increasing accuracy and reducing costs, it can help clinicians and healthcare administrators make better decisions that support a positive patient experience across virtually all healthcare settings. AI & automation in healthcare: key benefits  AI and automation deliver results in the three areas that matter most to healthcare organizations: improving the patient experience and care delivery, allowing staff to perform at their highest level, and increasing revenue. Boosting patient satisfaction through speed and accuracy Patient feedback has a few common themes: timely access to care, clearer communication and greater financial transparency. To meet these needs (and improve those feedback scores), healthcare providers should offer patients accurate, upfront information and reduce friction wherever possible. Tools like Patient Access Curator use AI to verify and update all necessary patient information at the front end, all at once, which drastically reduces the time and effort required to manage patient records. This streamlines patient intake and solves for bad data, which prevents claim denials and increases patient satisfaction. Bringing in more revenue by reducing claims errors The 2024 CAQH index estimates that 22% of current costs could be saved by shifting from manual revenue cycle processes to automated ones. Experian Health's State of Claims Survey 2024 suggests providers are eager to capitalize on this opportunity, with 51% seeking to reduce manual work. AI-driven solutions like Patient Access Curator and AI Advantage are designed specifically to meet these needs. Patient Access Curator automates insurance eligibility and coverage, scanning patient documentation for inaccurate information, and uses AI and robotic process automation to reduce manual errors. AI Advantage™ works to prevent denials before they happen, using predictive analytics to flag claims errors and alert staff to claims that fail to meet payer requirements. Improving staff performance by easing burnout The strain of manual processes doesn't just slow down operations. It's also a major cause of staff stress and burnout. Around half of healthcare staff report feeling burned out, costing the industry an estimated $4.6 billion each year. By taking repetitive tasks off busy employees' plates, AI can alleviate overwork and allow staff to focus on higher-value work, improving job satisfaction and productivity. In claims management, for example, AI Advantage, works in conjunction with ClaimSource®, to proactively identify claims with a high likelihood of denial prior to claim submission without staff intervention. This reduces the burden on staff while improving clean claim rates. How AI Advantage and Patient Access Curator improve patient care Experian Health's two flagship AI-based products go even further, offering new ways to use technology to improve patient care: Patient Access Curator uses AI and robotic process automation to streamline one of the most tedious parts of patient intake – verifying insurance eligibility and coverage. By automatically scanning patient records for errors and pulling up-to-date information from payer sources, it eliminates the guesswork and manual labor that bog down revenue cycle teams. The result is faster, more accurate eligibility verification and a smoother experience for both staff and patients. As Ken Kubisty, VP of Revenue Cycle at Exact Sciences, put it: “Within the first six months of implementing the Patient Access Curator, we added almost 15% in revenue per test because we were now getting eligibility correct and being able to do it very rapidly.”  On the back end, AI Advantage – Predictive Denials acts as an early warning system for denials, scanning claims before they go out the door to catch errors and flag risky submissions so they can be corrected in time. Built on advanced AI and machine learning, the platform evaluates claims using historical payment data and real-time payer behavior. Its counterpart, AI Advantage – Denial Triage, picks up where Predictive Denials leaves off, sorting rejected claims according to their potential for reimbursement and prioritizing them based on financial impact. Together, they help providers minimize denials, resulting in faster reimbursement and freeing up resources that can be redirected to patient care. Case study: See how AI Advantage helped Schneck Medical Center achieve a 4.6% average monthly decrease in denials in the first six months. The future of AI in healthcare: what's next? As a quick glance at any newsfeed will confirm, AI's role in healthcare is only going to expand. Predictive analytics will give staff increasingly powerful insights and recommendations to maximize reimbursements, while minimizing the burden on the workforce. AI's ability to continually learn and improve means providers that embrace AI will be better placed to make full use of their data and adapt to the trends and challenges that affect patient care. As expectations grow and resources shrink, AI is likely to be the only way to deliver the scalable, responsive, high-quality care patients deserve. Discover how solutions like AI Advantage and Patient Access Curator use artificial intelligence in healthcare to help reduce claim denials, improve patient access and more. AI Advantage Patient Access Curator

For patient access leaders at large healthcare organizations, the pressure is mounting and has been building for some time. Healthcare claim denials are climbing. Staffing is stretched, and the tools healthcare organizations have relied on for years are no longer enough. But what if providers could stop denials before they start? Welcome to the new era of denial prevention in healthcare, powered by predictive intelligence. Experian Health's innovative artificial intelligence (AI) solutions, Patient Access Curator and AI Advantage™, were designed to help organizations prevent denials before they occur. Explore how Experian Health is reshaping the way health systems manage Coordination of Benefits. Learn how automation and AI are eliminating manual errors, reducing denials and unlocking millions in recoverable revenue. Watch now > The denial spiral explained: A systemic challenge in revenue cycle management  Claim denials aren't just a back-end billing issue. They're a symptom of upstream breakdowns—often rooted in inaccurate or incomplete patient data at registration. According to Experian Health's 2024 State of Claims Survey, 46% of denials are caused by missing or incorrect information. And the cost of reworking a denied claim? $25 for providers and $181 for hospitals. The result? A denial spiral that drains resources, delays reimbursements, and frustrates patients and staff alike.  Why Epic users are especially vulnerable While Epic is a powerful EHR platform, many Epic-based organizations still rely on staff to make complex decisions at registration. Questions like: Is this coverage primary? Should discovery be run? Is this data accurate? ...are often left to frontline staff. This guesswork leads to inconsistent outcomes—and denials. What's needed is a layer of predictive intelligence that works within Epic to automate and correct data before it becomes a problem.  How Patient Access Curator fixes registration errors Patient Access Curator is that layer. Patient Access Curator is an all-in-one solution that automatically finds and corrects patient data across eligibility, Coordination of Benefits (COB) primacy, Medicare Beneficiary Identifiers (MBI), demographics and insurance discovery—within seconds. It integrates directly into Epic workflows, eliminating the need for staff to toggle between systems or make judgment calls on the fly. Instead of relying on registrars to catch every error, Patient Access Curator uses machine learning and predictive analytics to: - Identify and correct bad data in real time - Return comprehensive coverage directly into Epic - Reduce denials, write-offs, and vendor fees - Improve staff morale by removing administrative burden As one early-adopting Patient Access Curator client puts it: "If your current workflow still depends on frontline decisions, you're not just risking denials—you're building them in."  Predictive intelligence in healthcare: AI Advantage at work While Patient Access Curator fixes the front end, AI Advantage tackles the middle of the revenue cycle, where claims are scrubbed, edited, and submitted. At Schneck Medical Center, AI Advantage helped reduce denials by 4.6% per month and cut denial resolution time by 4x. The tool flags high-risk claims before submission and routes them to the right biller for correction. It also triages denials based on the likelihood of reimbursement, so staff can focus on the claims that matter most. Together, Patient Access Curator and AI Advantage form a closed-loop system: - Patient Access Curator ensures clean data at registration - AI Advantage predicts and prevents denials mid-cycle - Both tools integrate seamlessly with Epic and ClaimSource®  Why predictive denial prevention matters for patient access leaders  By implementing denial management technology and predictive intelligence, healthcare teams aren't just managing workflows; they're managing risk. Every inaccurate field, every missed coverage, every manual decision is a potential denial. Patient Access Curator and AI Advantage remove that risk by replacing guesswork with certainty. And the benefits go beyond revenue: - Fewer denials mean fewer patient callbacks and less frustration - Cleaner data means faster reimbursements and fewer write-offs - Automation means staff can focus on patients, not paperwork As Jason Considine, President at Experian Health, recently shared: "Our mission is to simplify healthcare. That starts by getting it right the first time, before a claim is ever submitted. With the power of AI and predictive intelligence, we're no longer waiting for denials to happen; we're helping providers proactively prevent them. Tools like Patient Access Curator and AI Advantage allow healthcare organizations to identify issues at the point of registration and throughout the revenue cycle, so teams can focus on care, not corrections. It's about working smarter, reducing risk and protecting revenue."  Denial prevention checklist: Preparing patient access teams for predictive denial prevention  Denial prevention is here, but what if billing teams aren't quite ready? To move toward a predictive denial prevention strategy, healthcare organizations can invest in the following five areas:   Audit front-end workflowsMap out every step from patient registration to claim submission. Identify where manual decisions are being made—especially around eligibility, COB, and insurance discovery. Ask: "Where are we relying on staff judgment instead of system intelligence?" Train staff on data quality awarenessReinforce the impact of inaccurate or incomplete data on downstream denials. Use real examples to show how a single missed field can lead to rework, write-offs, or patient frustration. Introduce the concept of "first-touch accuracy" as a team-wide goal. Evaluate Epic integration readinessAssess whether current Epic environments are configured to support automation tools like Patient Access Curator. Work with IT to assess whether the current setup allows for real-time data correction and coverage updates. Confirm that teams understand how new tools will integrate into their existing workflows, not replace them. Establish a denial prevention task forceBring together leaders from patient access, billing, IT and revenue cycle to align on goals. Assign ownership for key metrics like clean claim rate, denial rate, and registration accuracy. Use this group to pilot new tools like Patient Access Curator and AI Advantage and gather feedback from frontline users. Communicate the "Why" behind the changeFrame automation as a way to reduce burnout, not replace jobs. Highlight how tools like Patient Access Curator eliminate guesswork and free up staff to focus on patient care. Share success stories from peers (like Schneck Medical Center) to build confidence and momentum. The bottom line: Strategic denial prevention is the future Denial management is reactive. Denial prevention is strategic. For healthcare organizations using Epic, Patient Access Curator and AI Advantage offer a smarter, faster and more scalable way to increase reimbursements and improve the patient experience. Learn more about how Experian Health can help protect revenue, reduce staff burdens and reduce claim denials—starting at the first touchpoint. Learn more Contact us

When it comes to artificial intelligence (AI) usage in the healthcare industry, adoption is steadily gaining momentum as providers explore new ways to utilize this technology in their revenue cycle management (RCM) processes. While full trust in AI remains limited, especially for high-stakes decision-making, confidence is rising.   Privacy, security, and implementation costs continue to pose significant challenges. However, providers broadly agree that AI will become a cornerstone of healthcare RCM in the next few years, especially in areas like eligibility verification and patient access. Many also acknowledge that human oversight will remain essential, to ensure accuracy and trust.  In October 2025, Experian Health surveyed 200 healthcare leaders to better understand how much they trust AI for decision-making, their biggest barriers to adoption, and where the opportunities lie.    Here are the results: AI in healthcare RCM isn’t the future; it’s happening now. Learn how healthcare organizations are using Experian Health’s AI technology to streamline patient access and reduce claim denials. Learn more Contact us

Key takeaways: Efficient revenue cycle management is crucial to ensuring efficient hospital operations and building financial stability.   RCM technology solutions allow healthcare organizations to increase cash flow and improve operational efficiency across the entire revenue cycle.   Revenue cycle management tools from Experian Health utilize data-driven insights, automation and AI to optimize revenue cycles, while supporting compliance and regulatory needs.  The revenue cycle management market is projected to grow to $238B by 2030. Revenue cycle management is a critical process that ensures healthcare organizations maintain healthy cash flow and keep operations running smoothly. However, keeping the financial scales tipped in the right direction can be a never-ending challenge for revenue cycle leaders. As hospital profit margins remain tight, technology-based RCM solutions can help revenue cycle leaders stay ahead and maximize reimbursements. In this guide to revenue cycle management, providers will learn how to optimize revenue cycle processes at every stage of the patient journey. What is revenue cycle management (RCM) in healthcare? Revenue management in healthcare connects the financial and clinical aspects of patient care. The primary purpose of RCM is to help healthcare organizations ensure proper reimbursement and accurate, efficient billing and claims management processes. Seamless revenue cycle management also allows providers to maintain a solid financial foundation, a critical factor in boosting resilience during uncertain economic times.    Over the last few decades, RCM in healthcare has undergone numerous evolutions. Largely paper-based, manual processes have been replaced by sophisticated software-based systems and data-driven technology. As more organizations embrace the ongoing digital transformation of RCM in healthcare, processes now commonly include solutions that utilize machine learning, automation and artificial intelligence (AI). Leveraging technology boosts efficiencies, streamlines operations and allows organizations to see quicker reimbursement rates. However, despite these upsides, switching to new modern revenue cycle management systems isn’t always a priority for providers. Many healthcare organizations still partially rely on outdated and disjointed systems that can result in reimbursement delays and other snags in the revenue cycle. How the healthcare revenue cycle works A typical healthcare revenue cycle follows the step-by-step lifecycle of a patient encounter, known as the patient journey. Every touchpoint is an opportunity for revenue cycle teams to ensure that patients, payers and back-office teams have the information needed to expedite payment. Along with revenue cycle leaders, a wide range of healthcare staff are commonly involved in various administrative functions across the RCM cycle. Depending on the organization and how revenue cycle processes are set up, this may include front desk staff, scheduling teams, medical coders, billing staff and collections teams. While individual healthcare organizations often customize their exact RCM process, most revenue cycles are generally broken down into several key​​ phases of the patient journey: pre-visit, visit and post-visit. Each phase of the healthcare revenue cycle also has its own specific components, such as registration, claim submission and collections. Key stages of the revenue cycle process commonly include: Pre-visit: This phase includes all of the steps of the patient journey that happen before treatment, such as preregistration, patient registration, insurance verification and prior authorization.  Patient visit: The next phase includes revenue cycle activities related to the patient visit for treatment or services, such as documentation, coding and charge capture.   Patient post-visit: This phase includes the steps of the patient journey after care has been received, such as claims submission, collections, payment posting, and any necessary follow-up.   Detailed breakdown of each revenue cycle stage Successful healthcare revenue cycles consist of a series of stages. Each component of the RCM cycle is carefully designed to prevent revenue leaks and create a frictionless patient journey. Here’s a detailed breakdown of what happens during each revenue cycle ​​stage across the pre-visit, visit and post-visit phases of the patient journey: Patient registration: Gathers key patient information before service, including demographics, insurance, medical history and other personal details.  Eligibility and benefits: Verifies patient insurance coverage status, checks for additional or unknown coverage and provides transparent, accurate estimates prior to service. Data entry: Maintains accuracy of patient information data, verifying and protecting patient identities to ensure the right information is linked with the right patient. Prior authorizations: Determines if prior authorizations are needed before service, submitting payer requests as needed. Patient encounter: Adds information about the services a patient receives to the patient record, setting the stage for accurate coding and billing. Charge posting: Submits claims to relevant payers using the appropriate charge posting or charge entry process  — including a detailed breakdown of all services provided to the patient, patient information, history and insurance or payment plan status. Coding and billing: Checks payer codes for the services that have been delivered — using diagnostic (Dx) codes, place of service (POS) codes, current procedural terminology (CPT) codes, Healthcare Common Procedure Coding System (HCPCS) codes and others to determine payable amounts. Claims management: Submits claims and facilitates communication between providers and payers during the claims adjudication process, providing early intervention for denied claims and reworks as needed. Payer contract management: Ensures timely reimbursements — auditing payer performance, keeping track of changing requirements and identifying reimbursement discrepancies and patterns of non-reimbursement. Patient billing and collections: Bills patient for remaining amount owed after insurance reimbursement — collecting balances using in-house collections teams or outside collections agencies. ​​​Common challenges in revenue cycle management (RCM) To avoid escalating administrative costs and revenue leaks, teams must remain vigilant against challenges that disrupt the revenue cycle — including data errors, billing code mistakes, claims denials and payment delays. It can often be overwhelming for busy RCM leaders to sidestep obstacles. However, staying on top of challenges in revenue cycle management is critical to ensuring healthy cash flow and smooth-running daily operations.  Here’s a closer look at six common roadblocks RCM leaders need to keep tabs on: Incomplete documentation: Missing or outdated insurance information — and other missing or incorrect patient data — can lead to coding errors, claim denials and billing delays. Mistakes are most common when organizations use outdated manual processes, like paper forms. Coding errors: Mistakes made during billing code submissions — often due to error-prone manual processes or rapidly changing payer requirements — can lead to denials, delays and reworks. Claim denials: Claims denials are on the rise, leaving healthcare organizations to face potential hits to the bottom line from delayed or unpaid claims, while adding extra administrative burden for reworks. High days in accounts receivable: Collection delays are often a major roadblock in the revenue cycle — disrupting cash flow and potentially leading to extra administrative costs and bad debt.   Patient payment responsibility increases: Rising healthcare costs, including out-of-pocket expenses and high-deductible healthcare plans, put more financial burdens on today’s patients, leaving many struggling to pay their medical bills. Regulatory complexity: New price transparency regulations, implementation of the One Big Beautiful Bill Act, patient privacy safeguards under the Health Insurance Portability and Accountability Act (HIPAA), payer compliance changes and other rapidly evolving healthcare requirements can bottleneck revenue cycle processes and slow down reimbursements.  ​​​Strategies to improve healthcare revenue cycle performance Leaders tasked with improving healthcare revenue cycle performance can adopt RCM strategies that turn roadblocks into opportunities for growth. Here are five strategies to streamline RCM processes, boost performance and maximize revenue.  Implement automation Quick, accurate and efficient patient access processes are the foundation of healthy revenue cycles. Revenue cycle leaders should look at technology-forward solutions that leverage automation to boost efficiencies across all stages of the revenue cycle – from registration and scheduling to prior authorizations, claims processing and collections.  Utilize real-time eligibility checks Finding missing health insurance is critical to keeping revenue cycles on track. Real-time insurance eligibility verification allows providers to quickly confirm active coverage at any point in the revenue cycle, including additional coverage a patient may have forgotten.   Avoid solutions that require heavy staff training or certifications Revenue cycle management solutions that are easy to onboard and require little to no staff training or special certifications are often more efficient to implement and utilize — minimizing administrative costs and allowing busy staff to focus on other priorities.  Consider outsourcing vs. in-house billing Implementing tools that streamline key steps in the RCM process—like coding, claims submissions and collections—allows busy billing teams to maximize their time, save on administrative costs, accelerate collections, and avoid unnecessary outsourcing to third parties.  Choose the right metrics to ​​monitor Identifying and monitoring key revenue cycle performance indicators (KPIs) aligned to specific RCM priorities offers real-time insights into key stages across the revenue cycle — including patient access, collections, claims and contract management. Check out this guide to choosing the right key performance indicators for your revenue cycle dashboard to ensure the effective implementation of RCM ​​strategies. RCM in the era of modern technology and ​​AI Reimbursement delays commonly stem from error-prone manual revenue cycle processes. Overworked staff burdened by time-consuming administrative tasks related to RCM often further compound reimbursement issues. However, adopting solutions that utilize revenue cycle management automation, machine learning and AI allows healthcare organizations to overcome numerous pain points and ensure prompt reimbursement.   While constantly evolving, today’s top revenue cycle management technology often relies on:  Interoperability and data integration: Data-driven, turnkey revenue cycle management healthcare tools share data and function together seamlessly across the revenue cycle — using machine learning, automation and AI to constantly improve RCM.  Patient engagement tools and payment portals: AI-powered patient engagement tools and automated solutions improve patient access and accelerate collections rates.  Use of predictive analytics: Built-in predictive analytics offer actionable insights that improve patient access, claims processing, collections and other key areas of the revenue cycle.   Revenue cycle management case studies​​ Exact Sciences Ken Kubisty, VP of Revenue Cycle at Exact Sciences, shares how Experian Health’s Patient Access Curator helped their organization reduce claim denial errors and added $75 million in insurance company collections. Community Medical Centers Brandon Burnett, VP, Revenue Cycle at Community Medical Centers, shares how their organization partnered with Experian Health to implement AI Advantage™, which uses artificial intelligence to prevent and triage claims denials. Weill Cornell Medicine In a recent on-demand Webinar, we shared how Weill Cornell Medicine and Experian Health implemented a smarter collections strategy that delivered $15 million in recoveries using Collections Optimization Manager. Watch the on-demand webinar > How to choose the right RCM software or vendor Revenue cycle leaders who want to improve their organization’s RCM process often benefit from implementing RCM software or partnering with a vendor that specializes in healthcare revenue cycle digital solutions.   When choosing a solution, look for these key features: Patient access tools to improve registration, scheduling, estimates and payments. Insurance verification software with the ability to perform real-time eligibility checks and stay on top of ever-evolving regulations and payer policies. Claims management solutions that improve accuracy and efficiencies across claims submission and denials management processes. Collections software to streamline patient collections and reduce bad debt. Contract management tools to audit payer compliance against contract terms and maximize reimbursement rates. Data and analytics tools to monitor the revenue cycle, track key performance metrics and gain valuable insights. Questions to ask vendors Vetting vendors is critical to finding the best RCM software. Each healthcare organization has unique needs, so it’s important to vet vendors carefully to find the right revenue cycle management solutions.   Consider these questions when scoping out revenue cycle software solutions and vendors: Implementation: What’s the implementation process like? How does staff training and onboarding work?   Integration: Will the RCM software work with legacy systems? If not, what processes can it replace?   Customization: What types of customization options are available? What legacy systems are supported?   Scalability: How flexible is the solution? Does the RCM software have the ability to scale?   Usability: Is the software user-friendly? How easy is it to navigate the platform, share data and manage multiple stages of the revenue cycle?  Reporting: What types of reporting and analytics are built-in? Can KPIs be customized?   Cost: How does pricing work? Is it determined by functionality? Number of users? Size of organization?  Customer support: What type of customer support is available? Will the organization have a dedicated customer service representative? Are experts available to help customize the software for the organization's needs or analyze data? Build vs. buy decision-making Adopting technology to streamline revenue cycle management is often a large investment. Finding the best solution often comes down to the healthcare organization's unique needs, including budget, existing technology stack and other factors.   Ultimately, the tools chosen will have a significant impact on an organization’s financial and operational health, making the decision to build a custom solution or purchase turnkey RCM software a critical one. While there’s no “right” choice, revenue cycle leaders should consider the pros and cons and vet vendors carefully to help ensure long-term success. Why choose Experian Health for revenue cycle management​ Working with an industry-leading revenue cycle software solution partner, like Experian Health, allows healthcare organizations to modernize and speed up their entire revenue cycle management process. Experian Health offers a wide range of award-winning revenue cycle management tools that allow organizations to optimize every stage of the revenue cycle — from patient access to collections, claims management and payer contract management.   Robust automated solutions help organizations eliminate manual processes, submit cleaner claims, maximize collections and cash flow – all while staying compliant with the latest regulations and improving the patient experience at every stage of the journey. Experian Health’s built-in RCM analytics leverage data to analyze, track and further optimize performance. Turning revenue cycle roadblocks into opportunities for ​​growth Today’s healthcare revenue cycle leaders face more RCM obstacles than ever before — from increasingly complex billing processes and rising healthcare costs to frequent regulatory and payer requirements and staff shortages. However, providers also have unprecedented access to RCM technology solutions designed to streamline all stages of the revenue cycle management process. Healthcare organizations that embrace RCM software solutions – especially tools that use AI, automation and machine learning – can optimize revenue cycle management, boost overall financial resiliency and keep revenue flowing for many years to come.   Learn more about how Experian Health’s revenue cycle management solutions can help healthcare organizations generate more revenue and increase their bottom lines. Learn more Contact us

Key takeaways: Artificial intelligence (AI) is changing how healthcare organizations operate, but while most providers believe in its potential, adoption is uneven. As payers use AI to control costs, providers must apply the same technology to address rising claim denials and data quality challenges. Experian Health’s AI Advantage™ and Patient Access Curator™ help providers prevent denials, improve efficiency and strengthen financial performance. AI is transforming every part of healthcare. In the doctor’s office, it supports faster diagnoses and treatment decisions. At the front desk, it helps verify coverage and schedule appointments. And in healthcare claims management, AI’s ability to interpret vast amounts of data is changing how claims are reviewed, processed and paid. But transformation is not without challenges. As providers adopt AI and machine learning (ML) to improve care and operations, payers are using the same technology to control costs and make faster coverage decisions. According to the American Medical Association, 61% of physicians believe AI is increasing prior authorization denials. Strategic use of AI is the only way to keep pace and remain competitive. This article outlines what providers need to know about using AI in healthcare, including how Experian Health’s AI Advantage and Patient Access Curator use AI to prevent denials, improve efficiency and strengthen financial performance. Understanding AI technology and machine learning in healthcare AI refers to technology that performs tasks that require human-like reasoning, such as recognizing patterns, interpreting data and solving problems. It learns from experience, spots trends a human eye might miss and generates recommendations based on what users want to achieve. Machine learning is a subset of AI that improves performance over time, helping healthcare organizations turn their own complex information into practical insights. Because these models are trained on each organization’s unique data, they can adapt to local patterns and workflow differences, making their predictions far more accurate. Clinical and operational applications AI and ML are now used in many areas of clinical care, such as: Improving diagnosis by analyzing medical images with greater accuracy. Accelerating drug discovery by tracking side effects and treatment outcomes. Improving surgical safety and precision through robotics. Supporting patients in managing their own health through wearables and remote monitoring. On the operational side, AI helps staff work smarter and faster. Patient access teams use AI to verify insurance, forecast demand and manage scheduling, while revenue cycle leaders use it to reduce manual work and improve claim accuracy. Experian Health’s State of Claims 2025 report found that 69% of organizations using AI solutions have seen fewer denials or higher resubmission success rates, reflecting measurable gains in both efficiency and financial performance. Read the Q&A: How AI innovation is transforming healthcare revenue cycle management Experian Health executive David Figueredo gives a closer look at how we’re helping healthcare organizations use AI to tackle claim denials head-on. How AI technology in healthcare can prevent and reduce claim denials and boost financial performance Despite these gains, denials are still rising, revealing persistent problems with data quality. More than half of providers (54%) in the State of Claims report say claim errors are increasing, and 68% find it harder to submit clean claims than they did a year ago. On top of this, keeping up with payers’ use of AI is challenging. American Medical Association President Bruce A. Scott notes, “emerging evidence shows that insurers use automated decision-making systems to create systematic batch denials with little or no human review.” In contrast, on the provider side, around 90% of denials require manual rework, contributing to a widening technology gap that slows reimbursement and puts pressure on already stretched teams. Experian Health’s AI-based solutions can help close this gap. Capture accurate data from the start with Patient Access Curator. Patient Access Curator uses AI and machine learning to automate front-end eligibility and authorization workflows. It verifies eligibility, insurance coverage and reduces the data errors that often lead to downstream denials. Improving data quality before a claim is created helps organizations submit cleaner claims, reduce delays and deliver a better patient experience. Case study: Experian Health and Ohio Health See how Ohio Health cut denials by 42% with Patient Access Curator and solved claim errors at the source. Predict and prevent denials with AI Advantage In a recent webinar, Eric Eckhart of Community Regional Medical (Fresno) and Skylar Earley of Schneck Medical Center discuss how AI Advantage helped them take control of their denials management strategy and maximize reimbursement. “What really sold [AI Advantage] for me was that it’s looking at my data. It’s not looking at Skylar’s data in the Midwest. It’s looking at my data in central California. We have lots of little payers that do their own thing, and it’s learning from my information, my actual denials that are happening. If the payer shifts, the model’s going to follow that and let me know about it.”— Eric Eckhart, Director of Patient Financial Services at Community Medical Centers AI Advantage takes a two-pronged approach to reduce the risk of denials and expedite rework: AI Advantage – Predictive Denials examines claims before submission and calculates the probability of denial based on historical payment data and undocumented payer behavior in real-time. High-risk claims can be edited before submission to reduce the risk of denial. AI Advantage – Denial Triage evaluates and segments denials based on the likelihood of reimbursement and prioritizes the work queue accordingly. It learns from past decisions to formulate recommendations with increasing accuracy, so staff can focus on denials that will be most likely to yield results. Eric Eckhart of Community Regional Medical (Fresno) and Skylar Earley of Schneck Medical Center discuss how AI tools have helped them reduce denials. Watch now > Key challenges of implementing AI technology in healthcare Interestingly, the State of Claims survey suggests confidence in AI outweighs adoption. While 67% of providers believe AI can improve the claims process, only 14% use it to reduce denials. This suggests some caution around the practicalities of AI implementation. For smooth implementation of AI technology, providers should consider three essentials: Data quality: AI tools are only as good as the data they analyze. Partnering with a reliable third-party vendor can help providers ensure that data is error-free and usable. Integration: New tools must fit easily with existing workflows and systems. A single-vendor solution can mitigate interoperability issues. For example, AI Advantage fits seamlessly with ClaimSource®, reducing disruption. Compliance and security: Solutions must comply with data privacy and security regulations, like HIPAA, to avoid financial and reputational risk and maintain patient trust. FAQs What is AI technology in healthcare? AI in healthcare uses algorithms and machine learning to analyze data, support clinical decisions and automate administrative tasks. How does AI technology improve the healthcare revenue cycle? AI technology automates claim reviews, predicts denials and prioritizes claims to maximize reimbursement efficiency. How can healthcare providers start using AI for claim denial management? Tools like Experian Health’s AI Advantage and Patient Access Curator integrate with existing claims management workflows to predict and prevent denials, automate reviews and improve front-end accuracy for faster reimbursement. What’s next for AI technology in healthcare? As predictive analytics, natural language processing and automation advance, providers that use AI strategically will see greater efficiency and faster reimbursements. With payers and competitors accelerating their AI adoption, understanding where and how to apply these tools will be essential to staying adaptable and financially resilient. What’s next for AI technology in healthcare? As predictive analytics, natural language processing and automation advance, providers that use AI strategically will see greater efficiency and faster reimbursements. With payers and competitors accelerating their AI adoption, understanding where and how to apply these tools will be essential to staying adaptable and financially resilient. See how AI Advantage and Patient Access Curator are helping Experian Health’s clients transform healthcare operations. Learn more Contact us

Healthcare claims denials are on the rise, despite more than a decade of industry-wide technological advances aimed at improving claims management processes. However, in recent years, the introduction of artificial intelligence (AI) into the healthcare ecosystem has begun transforming how healthcare organizations manage patient access — and the entire revenue cycle. The State of Claims: 2025 Download the full report to uncover actionable strategies and see if AI is what can break the denial cycle for your organization. This article summarizes a recent webinar with Experian Health’s Vice President of Innovation, David ‘Fig’ Figueredo, and Kate Ankumah, Product Manager for Patient Access Curator™, as they break down how healthcare organizations can use AI to build scalable, data-driven revenue cycle solutions and deliver measurable value across the patient access ecosystem. Explore how Experian Health is reshaping the way health systems manage Coordination of Benefits. Learn how automation and AI are eliminating manual errors, reducing denials and unlocking millions in recoverable revenue. Watch now > Evolution of AI in healthcare For more than a decade, a progression of technology – mostly rooted in automation – has attempted to solve the issue of rising denials. Today, with the help of AI solutions, the process is shifting away from transactional activities to a more intelligence-driven approach. AI tools can be implemented at every stage of the revenue cycle to solve persistent challenges – like benefit coordination, eligibility verification, and claims management. And while most providers have the capability to add AI solutions, claims denials continue to climb. “With all of the investment by organizations like Experian Health and HIS system vendors, there still is a high prevalence of an issue with coordination of benefits and eligibility denials.”David Figueredo, Experian Health’s VP of Innovation Figueredo further points out that while revenue cycle leaders are aware of AI and its potential, they often remain skeptical of the technology or are unsure how to best leverage AI tools for denial prevention. Overcoming perceptions about AI Healthcare leaders sometimes struggle with negative perceptions around adopting AI solutions. Figueredo notes this is common, and wants organizations to know that with AI, “There’s a lot of power, hope and expectation around the use of applied technologies and automation in the revenue cycle process.” Concerns about implementing AI for revenue cycle management vary widely. However, according to the results of an Experian Health data study presented during the webinar, "accuracy and reliability” are often a top worry among healthcare organizations considering adopting AI technology. Other common concerns about leveraging AI solutions include data privacy and security, cost of implementation, staff resistance and labor risk, and lack of transparency. Healthcare organizations also want to base the decision to utilize AI on measurable results. Where in the revenue cycle has AI been implemented? How did it improve denial rates? Finding a path forward with AI AI offers healthcare organizations the potential to increase operational efficiencies, reduce administrative burdens, and reduce costs. While many revenue cycle leaders are most willing to place bets on using AI for patient eligibility verification and claims management, barriers to adopting AI still exist. Figueredo notes: “We’re seeing a lot of organizations that are interested [in AI], but also guarded about its use. Healthcare leaders typically have a specific goal in mind for using AI and want to see real-world results.” He reminds healthcare leaders that with AI, we “can do things we couldn’t do before – but it’s how it’s applied in solving things in the [revenue cycle] process” that really matters. For many healthcare providers, the question becomes: Does adding AI solutions to the revenue cycle provide acceleration? Improve patient access? Reduce the number of manual touches? Can AI do more of the work consistently so staff labor can be reapplied to other focus areas? Does AI help mitigate ongoing staff shortages? Will it cut costs for healthcare organizations already operating on thin margins? Adopting AI: RCM best practices When modernizing the revenue cycle, Figueredo reminds healthcare providers to have a clear set of guidelines and recommends ensuring AI solutions are designed to meet specific revenue cycle goals. Top priorities for healthcare organizations often include: Reducing manual interactions: While there are still some situations that require human intelligence to make decisions, countless simple tasks can be automated to minimize manual workload. Fixing issues on the front end: Early interventions to proactively correct potential issues with claims before they become a bigger problem, like incorrect patient demographics or eligibility information, can be critical to preventing denials. Supporting real-time integration: To avoid relying on batch auditing or poorly informed automated decision-making in the revenue cycle, HIS systems and patient access platforms, like scheduling and billing, must be designed to handle real-time corrections. Adopting AI for COB with Experian Health’s Patient Access Curator Turnkey AI tools, like Experian’s Health’s Patient Access Curator (PAC), allow healthcare organizations to implement a comprehensive patient access COB solution that touches every step of the revenue cycle process – starting with patient registration. PAC consolidates important functions like eligibility checks, MBI, demographics and discovery into one seamless solution to maximize clean claims and minimize denials, appeals and resubmissions. Kate Ankumah, Product Manager for Experian Health’s Patient Access Curator, explains: “We know that bad data is like a virus. If it starts bad, it ends up on the claim – even if you try to solve it mid-stream, it’s already saved somewhere. At the point of scheduling, at the point of registration, [with the Patient Access Curator], we’re giving you the most accurate data so that it can live and get accurate to the claim." Case study: Experian Health and OhioHealth See how OhioHealth cut denials by 42% with Patient Access Curator and solved claim errors at the source. Benefits of leveraging AI for COB and claims management Adopting COB solutions powered by AI and machine-learning, like Experian Health’s Patient Access Curator, healthcare providers can improve overall accuracy during claims processing on the front end – and at every step of the revenue cycle. And when errors are reduced from the start, healthcare organizations typically benefit from less wasted staff time, decreased denial volumes, accelerated denial management, and fewer contingency vendor fees – plus a better patient experience overall. Patient Access Curator is available now – learn how your healthcare organization can get started and prevent claim denials in seconds. Learn more Contact us

Key takeaways: As claims denial rates continue to climb, pressure is mounting on healthcare organizations to find new ways to reduce denials. Leveraging artificial intelligence (AI) and automation-based tools, like Experian Health's AI Advantage™ and Patient Access Curator™ solutions, is proven to lower denial rates. More than half of survey respondents say they’d replace existing claims management platforms if presented with compelling ROI to make the change. Claim denials are a well-documented challenge for healthcare organizations. Denied claims take much longer to pay out than first-time claims, if they get paid at all. Each one means additional hours of rework and follow-up, pulling in extra resources as staff review payer policies and figure out what went wrong. It’s time-consuming and costly. Beyond dollars and paperwork, denials affect patient care as uncertainty about payments leads to delays in treatment or unexpected out-of-pocket costs. But how do healthcare leaders feel about the state of claims management today? How are they tackling the administrative burden? Is there any light at the end of the denials tunnel? Experian Health surveyed 250 healthcare revenue cycle leaders to find out. The 2025 State of Claims report breaks down the survey findings, including insights into how automation and AI technology are being used (or not!) to optimize the claims process for denial prevention and increase revenue. The State of Claims: 2025 Download the full report to uncover actionable strategies and see if AI is what can break the denial cycle for your organization. What is the current denial rate for healthcare claims? Health claims are still stuck in a cycle of denials, delays and data errors. 41% of survey respondents said that at least one in ten claims is denied. That’s a lot of rework and lost revenue that providers were counting on. In 2009, claims processing accounted for around $210 billion in “wasted” healthcare dollars in the US. A decade later, the bill had climbed to $265 billion. Industry reports – including Experian Health’s State of Claims series – repeatedly observed a rise in denial rates. Today, 54% of providers agree that claim denials are increasing. And with this increase, providers constantly worry about who will pay – and when. What are the most common reasons for healthcare claim denials? According to the State of Claim survey respondents, the top three reasons for denials are missing or inaccurate data, authorizations and inaccurate or incomplete patient info. In short? The problem is bad data. 26% say that 10% of denials result from inaccurate or incomplete data collected at patient intake. Given how much information has to be processed and organized to fill out a single claim, this isn’t surprising. From patient information to changing payer rules, the sheer volume of data points to be collected and collated creates too many opportunities for errors and omissions. Other challenges, such as coding errors, uncovered services, eligibility checks, and staff shortages still play a role, but it’s clear that solving the data problem could make a meaningful dent in the denials problem. Blog: How data and analytics in healthcare can help maximize revenue Find out how the right data and analytics can help providers better understand their patients, streamline operations and improve revenue. Could automation improve claim denial statistics? To help break the denial spiral, more healthcare providers are turning to claims management software. Leveraging technology helps organizations resolve or prevent the snags that interfere with claims processing and billing workflows – boost claim success rates. That said, around half of providers still review claims manually. Yet, despite the proven benefits of integrated workflows and automation, the drive to implement new technology seems to have lost momentum. In 2025, 41% of survey respondents say they upgraded or replaced their claims management technology in the last year. However, 56% say that their current claims technology is sufficient to address revenue cycle demands – far below the 77% in 2022. While some tasks still genuinely require a human touch, staff time is often wasted on repetitive, process-driven activities that would be better handled through automation. Here are a few ways claims automation can help improve claim denial statistics: Connect the entire claims process end-to-end: Using an automated, scalable claims management system – like ClaimSource® – helps providers manage the entire claims cycle in a single application. From importing claims files for faster processing to automatically formatting and submitting claims to payers, it simplifies the claims editing and submission process to boost productivity. Submit more accurate claims: 68% of survey respondents say submitting clean claims is more challenging than a year ago. There’s a strong case, then, for using an automated claim scrubbing tool to reduce errors. Claim Scrubber reviews pre-billed claims line by line so errors are caught and corrected before being submitted to the payer, resulting in fewer undercharges and denials and better use of staff time. Improve cash flow: Automating claim status monitoring is one way to accelerate claims processing and time to payment. Enhanced Claim Status eliminates manual follow-up so staff can process pended, returned-to-provider, denied, or zero-pay transactions as quickly as possible. Reduce denials: Denials Workflow Manager automates the denial process to eliminate the need for manual reviews. It helps staff identify denied claims that can be resubmitted and tracks the root causes of denials to identify trends and improve performance. It also integrates with ClaimSource, Enhanced Claim Status and Contract Manager, so staff can view claim and denial information on a single screen. Improving claim denial statistics with AI While automation speeds up the denials workflow by taking care of data entry, AI can examine that data and recommend next steps. Of the 14% of survey respondents who said their organization is currently using AI, 69% say that AI solutions have boosted claims success rates, reducing denials and/or increasing the success of resubmissions. Current ClaimSource users can now level up their entire claims management system with AI Advantage, which interprets historical claims data and payer behavior to predict and prevent denials. The video below gives a handy walk-through of how AI Advantage’s two offerings, Predict Denials and Denial Triage, can help providers respond to the growing challenge of denials. Additionally, turnkey AI solutions, like Patient Access Curator (PAC), allow organizations to ensure claims are processed accurately from the start. Introduced in 2025, PAC is a groundbreaking tool that consolidates important functions like eligibility checks, MBI, demographics and discovery into one seamless solution – maximizing clean claims and minimizing denials, appeals and resubmissions. Explore how Experian Health is reshaping the way health systems manage Coordination of Benefits. Learn how automation and AI are eliminating manual errors, reducing denials and unlocking millions in recoverable revenue. Watch now > Can AI break the claims denial spiral? Technology is critical to improving claims management processes, and 59% of survey respondents say they plan to invest in claims management technology in the next six months. Leveraging AI for claims management could break the cycle of denials, but is healthcare ready to trust it? Despite a solid understanding of AI’s potential, survey findings suggest many healthcare organizations still have concerns. According to the data, top worries include its accuracy, HIPAA compliance, the need for staff training on new technology, and AI’s understanding of payer-specific rules. However, as claim denials continue to rise, organizations that make the leap to adopt technology-based solutions that leverage automation and AI could prevent more denials and level the playing field with payers. Download Experian Health’s 2025 State of Claims report for an inside look at the latest claim denial statistics and industry perspectives on claims and denials management. Get the report Contact us

Healthcare organizations are facing a perfect storm: rising claim denials, evolving payer rules, and patients expecting providers to reduce error rates that impact patient billing accuracy. Artificial intelligence (AI) has raised the stakes, causing revenue cycle leaders to feel the pressure to modernize quickly. According to Experian Health's State of Claims 2025 survey, 73% of providers agree that claim denials are increasing, which is a clear signal that outdated processes cost providers millions. The top-ranked reasons for denials included coding errors, missing or inaccurate data, authorizations, and incomplete information, to name a few. And with only 14% of providers using some form of AI technology in their processes, the message is clear:  the opportunity is high to get more providers to embrace the technology and reap the benefits of smarter automation. To stay competitive and financially viable, healthcare organizations must embrace AI-driven innovation that improves data accuracy, streamlines workflows and proactively prevents revenue leakage. To explore how leading RCM companies are responding, we interviewed David Figueredo, Experian Health's VP of Innovation, to get a closer look at how we're helping healthcare organizations use AI to tackle these challenges head-on. Meet the Executive David Figueredo, VP of Innovation at Experian Health, has spent over 20 years driving transformation in healthcare finance. Known for blending tech-forward thinking with operational expertise, David is passionate about using AI to solve persistent challenges in revenue cycle management, especially around claim denials and data accuracy. He believes that healthcare innovation must be both purposeful and scalable. "We're not just chasing trends, and buzzwords do not functionally solve problems," he says. "By focusing on building systems that adapt to payer behaviors and addressing the labor costs and manual inefficiencies providers face today, we can deliver measurable improvements in financial performance." David is passionate about building tools that empower revenue cycle teams to work smarter, not harder. "We're not just layering tech on top of broken processes," he says. "We're redesigning the workflows themselves to intuitively account for these emerging AI capabilities and by doing so, we are finding ways to fundamentally change those processes." Q1: "David, let's start with the big picture. How are you and your team thinking about innovation in revenue cycle management right now?" David: "At Experian Health, innovation is a strategic imperative, and the core to everything we do. We're focused on solving revenue cycle pain points, especially around claims management and patient access by blending AI, automation, and data intelligence to streamline workflows. We're not just trying to overlay new tech on yesterday's processes; we're reimagining how revenue cycle teams will operate, to reduce manual touch points and increase automated decisioning. That means leveraging AI to automate repetitive tasks, enable earlier and continuous monitoring with timely corrections, and equipping teams with actionable workflows backed by trustworthy, transparent insights. We're also seeing a shift in mindset and attitudes around automation and applied AI. Innovation used to be a long-term goal that took years to see measurable outcomes. Now, it's a short-term mandate where the pace of progress needs to deliver value today and increased value tomorrow. Our clients expect to see and feel the progress now, not just the promise of value in years to come. That's why we've designed a modular solution that allows clients to deploy AI tools where they deliver the most immediate value, while also supporting more complex workflows and integrations for the future. This includes integrating intelligence to improve eligibility checks, coordination of benefits (COB) and identity functions, enhancing claim scrubbing processes with accurate denial prediction and prioritization, and strengthening financial decisions with better data modeling that builds trust. Innovation should be cross-functional. This means aligning product design with IT build processes to reduce deployment times and mitigate risks, incorporating operations teams to ensure the right problems are being addressed, and enabling finance teams to better understand how technology impacts primary and secondary revenue streams." Watch our on-demand webinar to learn how healthcare organizations are using AI to eliminate manual payer chaining, detect and correct coverage issues in real-time, and reduce claim denials. Watch now Q2: "AI is everywhere these days, but how are you actually using it to reduce claim denials and improve data accuracy?" David: "AI can be a game-changer, but there is more to solving problems than just applying new technology. According to Experian Health's State of Claims 2025 report, 41% of respondents say their claims are denied more than 10% of the time. And 54% agree that errors in claims are increasing. We have to be thoughtful in how and where we apply AI to improve learning on the fly, promote integrated decision support in real time and automate actioning so that highly skilled and limited staff can focus on higher-value functions. AI is not just about automation; it's about intelligent intervention applied to real problems, removing guesswork, early issue identification and eliminating missed steps to improve the overall yield of the revenue cycle. Consider the denial space, where billions in revenue are lost each year. While the causes of denials are very diverse, many of them are excellent opportunities for applied AI to improve denial rates. Our flagship product, Patient Access Curator™, uses AI to address key drivers, such as eligibility and COB errors that account for 15-30% of all denials. AI can surveil system and user activity to detect missed coverage or primacy issues, then pursue those leads and update the HIS in real-time — both at registration and at every other touchpoint in the patient journey. Another great example of applied AI is our AI Advantage™ denial prediction and triage solution. While claim denial screening and prioritization are not new concepts, AI takes this to a new level by integrating behavioral analytics, machine learning processes and big data analytics into a simplified process. This solution doesn't just detect denials; it prioritizes them based on financial impact and likelihood of denial recovery, driven by a larger decision support framework that improves accuracy and reduces noise. Revenue cycle teams can then focus on high-value, revenue-protecting activities, rather than low-yield procedural work. Our models continuously learn from evolving payer behaviors as they emerge, to predict denial risk and recommend corrections in real time. And because they're continuously learning, they get smarter and vastly more adaptive than legacy ways of prioritizing pre-denial and denial workflows. It's a dynamic system that evolves with the payer landscape that maximizes limited resources, which I think is the hope and expectation of modern, AI-driven revenue cycle processes." Q3: "Can you give us a sense of the impact? What kind of results are clients seeing with AI tools?" David: "Absolutely. We are seeing some amazing early data that clearly point to very differentiated outcomes over traditional technology approaches. Since deploying our AI-driven denial prevention engine, we've seen a 15-60% reduction in initial eligibility and COB claim denials, with an average performance of ~30% reduction across our client base. However, the impact is not just on claim denials; we have to understand there are populations of patients, such as self-pay patients, that benefit from improved automation and intelligence that AI applied correctly can bring. We are also seeing significant reductions in self-pay at registration rates when AI is driving the automation. Here, we see ~25% reductions in self-pay at the time of registration. This is relevant and striking on so many levels, as correct estimates can now be provided pre-service, and authorization processes can now work more effectively, which leads to better patient experiences. What's most impactful is how these results compound over time. As AI tools mature, they start identifying systemic issues—like recurring documentation gaps or payer-specific quirks—that manual reviews often miss. That insight enables clients to fix individual claims while optimizing workflows and upstream processes, leading to long-term gains in efficiency and revenue integrity." Learn how Patient Access Curator streamlines patient access and billing, prevents claim denials, improves data quality, and makes real-time corrections to boost your healthcare organization's bottom line. Q4: "Let's talk about the patient side. A lot of innovation is happening behind the scenes, so how does that translate into a better patient experience?" David: "That's a great point. A lot of what we do in revenue cycle innovation isn't visible to patients, but it absolutely impacts their experience. In many cases, our patients are the victims of broken processes and fragmented data that AI and related technology improvements will help to resolve. Take claim denials, for example. When a claim is denied because of a missing authorization or incorrect insurance information, it doesn't just delay payment; it creates confusion and stress for the patient who may suddenly receive a surprise bill for something outside of their control. Resolving this issue requires multiple calls to the provider or payer, which adds frustration. This creates a stressful experience and negatively impacts the provider's brand perception. That's where AI makes the difference. We use Experian Health's AI-powered registration optimization and claims management tools, like AI Advantage, to catch these issues early, before the incorrect estimate is generated, before the authorization is missed or before the claim is submitted. This drives more consistency and automation into the revenue cycle. By improving data accuracy at the front end—with things like insurance verification, COB issue detection, automated coverage surveillance and predictive analytics — we're helping providers get it right the first time. The result: fewer billing surprises, faster resolutions and a smoother patient journey. While the patient may not see the AI working in the background, they feel the difference when their estimates are more accurate, duplicate or conflicting statements are reduced, and they no longer have to chase down answers. This builds trust and improves patient satisfaction – allowing them to focus on their health, rather than revenue cycle issues they should never have to deal with." Q5: "For healthcare organizations that are just starting to modernize their revenue cycle, where should they begin?" David: "Start by understanding your internal views, change threshold and restrictions. Many healthcare providers don't ask hard questions about their goals, the data they're willing to share or how to prioritize their needs. AI is only as good as the data it has access to, so ensure your data is clean, structured, and compliant with legal and clinical requirements. Next, find partners with the right technical tools and healthcare experience. Focus on measurable outcomes —not just technology—and prioritize areas with the greatest revenue leakage, high FTE investments or elevated patient risk. Don't underestimate the importance of change management. Involve your operations, training and strategy teams early, and make them part of the innovation process. Overemphasize the human element of change control to improve outcomes. Finally, always keep the patient in mind. Every improvement in the revenue cycle affects their experience and access to care. Design technology solutions that simplify the patient journey, reduce their burden, and help lower the cost of care." The future of RCM lies in AI innovation  As healthcare organizations navigate mounting financial pressures and the increasing complexity of payer requirements, the need for smarter, AI-powered solutions has never been greater. By embracing intelligent automation, providers can reduce costly errors and denials, strengthen their financial stability and enhance patient experiences. Learn how Experian Health's AI-driven solutions, like Patient Access Curator and AI Advantage, can help your healthcare organization minimize claim denials, streamline workflows and unlock new opportunities for financial success. Learn more Contact us