Tag: Revenue Cycle Management

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

"We knew we needed to transform our authorization workflow processes. We were experiencing a high rate of denials due to a lack of authorizations."- Amy Grissett, Senior Director of Ambulatory Revenue Cycle at USA Health Challenge: Manual processes that couldn't keep up Serving more than 250,000 patients each year across hospitals, specialty centers and outpatient clinics means USA Health processes hundreds of thousands of authorizations. Speed is critical. Unfortunately, small inefficiencies were taking a major toll. Frustrating manual authorization processes resulted in work queue errors, forcing staff to print schedules multiple times a day to keep track of changes. Inevitably, cases were missed, resulting in claim denials and delays. It was hard to see where to make improvements without a reliable way to monitor staff performance. As new service lines were added and authorization requests grew, USA Health needed to find a more efficient way of handling authorizations, or overworked teams would be under even more pressure. Amy Grissett, Senior Director of Ambulatory Revenue Cycle at USA Health, says, "We knew we needed to transform our authorization workflow processes. We were experiencing a high rate of denials due to a lack of authorizations." Since hiring extra staff had been ruled out, automated prior authorizations were the obvious solution. Solution: Automating authorizations for faster, more efficient workflows Having already worked with Experian Health for eligibility, USA Health decided to implement Authorizations to optimize their workflows and automation. Alicia Pickett, Senior Product Manager at Experian Health, explains how this partnership worked: "First, the team needed to determine if authorization was necessary. If so, they would complete the authorization on the payer's website. Experian Health's Authorizations would then track the status of the authorization, saving time on phone calls and web portals for pending cases. Once the authorization was obtained, our product would automatically post the status update into the EHR." Automating status inquiries this way meant staff no longer needed to chase information through phone calls and payer portals. Dynamic work queues and alerts would guide them to priority tasks, allowing them to work more efficiently and accurately. Most importantly, authorized services could be cleared without delay. The tool also compares authorized procedures to those actually performed and flags any variance, so staff can amend claims submissions and prevent denials. "The implementation process took approximately 6-8 months, and we did it in phases," Grissett explains. "We started with one service line. As the team became more comfortable, we added additional service lines. Overall, the implementation met our expectations. And the solution has greatly improved our authorizations process and workflows." Outcome: Authorizations up, denials down Since implementing Authorizations, USA Health has seen measurable improvements, including: Increased daily authorizations by 100% Cut manual work by 50% and reduced errors and denials Expanded to six service lines without increasing staff Provided accurate tracking of staff productivity Instead of relying on slow, manual processes, staff now have thirty dynamic work queues at their fingertips, helping them process 130,000 authorization requests each year. Thirty dynamic work queues organize tasks by date and service line in real time. With automated payer website checks now delivering instant updates for more than half of all accounts, they can focus on the smaller number of complex cases that need hands-on management. The impact on productivity is clear. With the new workflow in place, the average number of accounts completed per employee each day has more than doubled, from around 20 accounts to between 40 and 50. In addition to monitoring accuracy and denial rates, Authorizations' monthly scorecards make it easier to measure staff performance. Grissett says, "We were trying to do more with less. We also wanted to be able to monitor what our employees were doing and ensure they were accountable. The tools that Experian provides allow us to capture that data." All of this benefits patients, too: With automated prior authorizations, fewer appointments are canceled or rescheduled because of authorization delays, so patients don't have to wait for care. "The Experian team was instrumental in helping us pivot and develop specific workflows tailored to our needs. Together, we addressed missing payer connections and created knowledge-based rule sets to drive efficiencies. As we add new facilities or services, the process is fairly seamless. We already have the intel on the number of staff required to manage a specific number of accounts, the productivity measures needed and how to streamline processes. This allows us to replicate workflow processes and optimize operations effectively. In fact, we've added six more departments with our staff of 28." - Amy Grissett, Senior Director of Ambulatory Revenue Cycle at USA Health Looking ahead, the team plans to introduce more service lines and facilities while continuing to refine workflows and streamline processes. Find out more about how Experian Health's automated prior authorizations can help your healthcare organization boost productivity, reduce errors and prevent costly denials. Learn more Contact us

Key takeaways: Healthcare organizations are facing growing levels of bad debt and a sharp decline in collections. Propensity-to-pay models that utilize machine learning and robust data offer insight into a patient's likelihood to pay and allow staff to focus their collections efforts where they matter most. In 2024, Experian Health clients that implemented Collections Optimization Manager saw a 10:1 ROI. Some clients, like Weill Cornell Medicine, have seen up to $15 million in recoveries. Healthcare organizations are facing a sharp decline in collections and an increase in bad ​​debt. Rising self-pay costs and more patients struggling to afford their medical bills are contributing factors. Inefficient collections practices, reliance on third-party agencies that don't utilize propensity to-pay scores and manual processes are also key contributors to this growing market problem. Providers who adopt propensity-to-pay models that use data and automation to forecast the likelihood of payment often see both improved revenue recovery and patient satisfaction. Here's what to know about propensity-to-pay collections strategies in healthcare. Why propensity to pay matters in healthcare collections "Propensity to pay" is a data-driven model that identifies patient populations with the greatest likelihood of paying, to enhance existing collection strategies. When billing teams better understand a patient's propensity to pay, they can easily prioritize outreach and allocate collections resources effectively. This eases their workload, as they can focus their efforts where they'll have the greatest impact, and on accounts with the highest probability of payment. Keeping more collections in-house also reduces the reliance on expensive third-party agencies, while eliminating wasted effort on low-yield tasks – like repeated phone calls or mailed statements to accounts unlikely to pay. The need to adopt propensity-to-pay models has grown in recent years as patient volumes and the cost of care continue to grow. In the last 20 years, U.S. hospitals have absorbed nearly $745 billion in uncompensated care, according to American Hospital Association data.American Hospital Association Rising healthcare costs and the newly enacted "One Big Beautiful Bill Act" are expected to shift even more financial responsibility to both hospitals and ​​patients. Unfortunately, many organizations still rely on inefficient collections processes, third-party agencies and medical billing practices that lack propensity-to-pay insights. The result? Disruptions to the entire revenue cycle, including lost patient revenue, wasted resource hours, increased costs to collect, and high vendor costs. Using outdated collections strategies also contributes to patient dissatisfaction and churn, causing even more revenue leaks. Why healthcare providers need propensity-to-pay analytics Limited staff capacity and high volumes of self-pay accounts further compound collections challenges for organizations that have yet to adopt propensity-to-pay analytics. As collections timelines drag out, providers can be left with cash flow issues, revenue losses and bad debt. This ultimately disrupts the revenue cycle and affects the quality of patient care – and the entire patient experience. By leveraging propensity-to-pay analytics, revenue cycle leaders can boost revenue cycle predictability and streamline collections efforts. Listen in as Weill Cornell Medicine and Experian Health discuss how a smarter collections strategy delivered $15M in recoveries – and how you can do the same. This on-demand webinar shows how to move faster, work smarter and collect more, without adding headcount. Watch now > How propensity-to-pay models work in practice Propensity-to-pay models screen and segment patient accounts based on the likelihood of payment. Segmented accounts receive a propensity-to-pay score – from 1 to 5, with 1 being the highest likelihood to pay — and are then transferred to appropriate reconciliation channels. Experian Health's solution, Collections Optimization Manager, leverages machine learning, predictive analytics and data sources – like credit, behaviour and demographics – to identify which patient accounts have the highest likelihood to pay. It also automatically screens patient data for deceased, bankruptcy, Medicaid and ​​charity. Patient accounts are then sorted into pay groups through data-driven segmentation. This allows busy collections staff to quickly clean up accounts receivable and put their focus where it matters most – patient accounts with the strongest chance of paying their bill. With a clear picture of a patient's financial situation, healthcare organizations can improve patient communication and further boost collections efforts to maximize revenue. High-propensity accounts may receive light-touch reminders, like less frequent bill reminders. At the same time, alternative financial assistance, such as charity care or payment plans, can be made available automatically to low-propensity patients. Benefits of using propensity-to-pay models Propensity-to-pay models, like Experian Health's Collections Optimization Manager solution, offer numerous benefits to organizations that strengthen the revenue cycle. Higher collections rates: Using a propensity-to-pay model makes AR more manageable, especially for high-patient-volume organizations. Complimentary tools, like Experian Health's PatientDial and PatientText, easily send self-pay options via voice or text message, boosting patient engagement and building trust. Reduced bad debt: Propensity-to-pay models help identify patients with a low likelihood of paying their medical bills. Lower collections costs: Chasing payments on accounts that are deceased, bankrupt, or eligible for Medicaid or charity wastes valuable resources. With propensity-to-pay models, busy staff can efficiently work on high-yield accounts in-house, reducing the number of accounts that need to go to third-party vendors. Faster cash flow: Prioritize likely-to-pay patients early and shorten payment cycles, which can improve revenue cycle predictability.  On-demand webinar: Boost self-pay collections – Novant Health & Cone Health’s 7:1 ROI & $14M patient collections success Hear how Novant Health and Cone Health achieved 7:1 ROI and $14 million in patient collections with Collections Optimization Manager.  Case study: How Wooster Community Hospital collected $3.8M in patient balances with Collections Optimization Manager Read more about how automated collections strategies helped Wooster Community Hospital achieve a $3.8 million increase in patient payments. Implementing propensity-to-pay analytics: Best practices Healthcare organizations that implement propensity-to-pay analytics should consider the following best practices:  Choose the right partner. Look for a technology partner, like Experian Health, with extensive data assets and healthcare expertise. Automate patient communication. Reduce overhead and increase collections efforts with automated patient communication strategies. Ensure alignment with legacy technology. For real-time accuracy, choose a solution that integrates seamlessly with existing EHR and billing systems. Train billing staff. Provide comprehensive training to billing and collections teams on propensity-to-pay scores and how to communicate payment options with empathy. Automate the agency management. Reduce the manual workload of auditing agency remittances by automating the reconciliation process. Monitoring patient accounts. Look for a solution that regularly scans for changes or updates in a patient's ability to pay or contact information. Track performance. Monitor key performance indicators to fine-tune the collections process over time and improve forecasting. How Experian Health's solutions support better collections Changing longstanding collections practices is often a significant investment. Yet, the cost of inaction is often greater. Experian Health's Collections Optimization Manager uses propensity-to-pay models, driven by machine learning, and data-driven workflows to help healthcare providers improve patient collections. Our comprehensive industry-leading solution offers a smarter and faster way to collect patient payments, and Experian Health's experienced consultants are there every step of the way, as collections needs shift. Learn more about how Experian Health's data-driven patient collections optimization solution helps revenue cycle management staff collect more patient balances. Learn more 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

Key takeaways: Experian Health’s State of Claims 2025 report is out now, detailing providers’ views on claims management and how these have changed since the survey began in 2022. Claim denials are still on the rise, causing providers to find faster and more efficient ways to submit clean claims the first time. When it comes to solutions, optimism about artificial intelligence (AI) is high, but uptake remains surprisingly low. AI-powered tools like Patient Access Curator™ and AI Advantage™ can help healthcare providers reduce claim denials while optimizing the claims management process. According to Experian Health’s State of Claims 2025 report, claim denials continue to negatively impact America’s healthcare providers. This quantitative survey of 250 healthcare professionals, carried out in June and July 2025, reveals providers’ concerns about rising denial rates, staffing shortages and uncertainty over whether payers or patients will ultimately pay. The findings show that providers are open to new claims processing and denial reduction solutions. However, while providers are enthusiastic about artificial intelligence's ability to ease the squeeze, only a small fraction are actually using it. This article highlights a few key takeaways from healthcare providers' statements about the current challenges in claims management and the factors that contribute to their responses. NEW: State of Claims 2025 Report Download the State of Claims 2025 report to see the full findings. Takeaway 1: Claim denials are on the rise again This year’s survey confirms what providers have been seeing for several years: claim denials are not letting up. In 2022, 30% reported that at least 10% of their claims were denied. By 2024, the figure had grown to 38%. Now, in 2025, 41% of providers say their claims are denied over 10% of the time. If this trend continues, how much further could denial rates climb? Claim denials are becoming a growing part of everyday operations, demanding more time, staff and resources. Margins that are already under pressure are strained further by missed reimbursements. And when insurers don’t pay, more of the bill falls to patients, many of whom are already struggling to manage medical costs. Half of respondents said they are “very or extremely concerned” about patients’ ability to pay, up six percentage points from last year. For many organizations, the question is not whether denials will continue, but how best to prevent them before the financial burden worsens. Blog: Denial prevention - Why manage denials when you can prevent them? Read more about how our claims management solutions help providers build effective denial prevention strategies and reduce lost revenue. Takeaway 2: How bad data leads to more healthcare claim denials The report lists several of the top triggers for denials, but inaccurate and incomplete data continue to stand out. More than half of providers (54%) say claim errors are increasing, and nearly seven in ten (68%) report that submitting clean claims is more challenging than it was a year ago. Many of these errors originate at registration. Incomplete or inaccurate information collected during check-in is now the third most common cause of denials, with 26% of respondents saying that at least one in ten denials at their organization can be traced back to intake errors. Every mistake sends ripples downstream, leading to costly rework, avoidable payment delays and unnecessary patient stress. Tightening up patient access processes and accurate data collection is one of the best things providers can do to curb denials. With that in mind, Experian Health’s Patient Access Curator is designed to help providers capture accurate data the first time. Using AI and machine learning, it consolidates eligibility checks, coordination of benefits, Medicare Beneficiary Identifier (MBI) verification, demographics, insurance coverage and financial status into a single workflow. This allows providers to: Quickly collect accurate patient information upfront Eliminate the need to re-run eligibility checks, which now take more than 10 minutes for over half of providers Reduce manual data entry errors that lead to downstream denials Free up staff time for higher-value tasks Case study: Experian Health & OhioHealth See how OhioHealth cut denials by 42% with Patient Access Curator and solved claim errors at the source. Takeaway 3: An AI paradox in healthcare claims: High optimism, low adoption Patient Access Curator is a great example of how AI can help address the data problems behind denials. But clean data alone isn’t enough. Errors and risks still emerge mid-cycle. Here, AI Advantage offers another application for AI, using predictive analytics to identify high-risk claims before submission and routing them for correction. It also triages denials based on the likelihood of reimbursement, so staff don’t lose time on unproductive rework. 69% of healthcare providers who use AI say that AI solutions have reduced denials and/or increased the success of resubmissions.State of Claims 2025 report | Experian Health The survey shows many providers are enthusiastic about AI's potential: 67% believe AI can improve the claims process, and 62% are very confident in their understanding of how AI differs from automation and machine learning, up sharply from just 28% in 2024. Despite this optimism, adoption is surprisingly low. Only 14% of providers are currently using AI to reduce denials. The survey suggests that even though the majority of AI adopters report fewer denials and more successful resubmissions, fear of the unknown seems to be slowing progress. Blog: Leveraging artificial intelligence for claims management Read more about how our AI-powered claims management solutions help healthcare providers improve reimbursement rates and reduce denials. Takeaway 4: Tech upgrades aren’t enough without integration Even if they remain on the fence about AI, providers are still moving to modernize claims management. Only 56% believe their current technology is sufficient to handle revenue cycle demands, a major drop from 77% in 2022. This explains why 55% are willing to completely replace their existing claims management platform if presented with a compelling return on investment. Much of the frustration comes from fragmentation. Nearly eight in ten providers say their organizations still rely on multiple solutions to collect the information needed for a claim submission. Switching between systems slows down intake, creates duplication and increases the risk of errors that feed directly into denials. An integrated solution like Patient Access Curator solves this problem by replacing a patchwork of tools with a single platform that manages intake and eligibility in one workflow. Information is captured in one place, reducing the duplication and errors that are inevitable when data is entered into multiple databases. Extending this with AI Advantage links front-end accuracy with back-office intelligence, giving providers a connected denial-prevention system rather than stitching together isolated fixes. With fewer tools to log into, staff can work more efficiently and focus on submitting cleaner claims. 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 > Closing the technology gaps in claims management to prevent denials The 2025 State of Claims report clearly shows that denials remain a persistent and costly problem for healthcare organizations. An overwhelming majority say that reducing them is a top organizational priority. Beyond the financial concerns, the survey reveals a system still held back by data errors, fragmented technology and delays. At the same time, there are hints of cautious optimism. Last year, many providers felt in the dark about AI and machine learning. This year’s survey shows that awareness of these technologies has grown considerably, even if adoption is still early. As the report sheds light on how leaders are weighing investments in new technology, the question now is whether providers can turn growing confidence in AI into action that delivers the results they need. To see the full picture of where claims management stands today, and where it could go next, download the State of Claims 2025 report. Download now Contact us

“Registrars used to wonder, ‘Do I run Coordination of Benefits? Which insurance is primary?’ Now Patient Access Curator does all that work and removes the guess work, and it does it in under 20 seconds.”Randy Gabel, Senior Director of Revenue Cycle at OhioHealth Challenge OhioHealth faced rising denial rates and inconsistent insurance discovery. Registrars relied heavily on what patients told them at check-in, without knowing if that information was complete or current. Forced to make judgment calls about whether to run Coordination of Benefits (COB) or check for Medicare Beneficiary Identifiers (MBI), staff could do little to avoid errors and denials. Randy Gabel, Senior Director of Revenue Cycle at OhioHealth, says, "We were sending claims with the wrong insurance simply because staff didn't know what to do next." They needed a reliable solution to identify coverage upfront – without asking patients to dig out old insurance cards or involving costly contingency vendors. OhioHealth's search became more urgent when a nationwide cyberattack hit the industry in early 2024. They needed a trusted revenue cycle partner to close the gaps in claims and eligibility workflows and prevent denials from the start. Solution To strengthen front-end revenue cycle operations, OhioHealth selected Experian Health's Patient Access Curator® (PAC). This all-in-one solution uses artificial intelligence (AI) and machine learning to check eligibility, COB, MBI, demographics and insurance discovery through a single process. This solution gave staff more accurate data in real-time. Although they had not worked with Experian Health before, the OhioHealth team was immediately convinced that Patient Access Curator fit the bill. Gabel says that during the evaluation, "Patient Access Curator discovered a whopping 18% more insurance on self-pay accounts than our current vendor. No other company or product found that much." PAC fits directly into existing workflows, so OhioHealth's 800+ staff members did not have to learn a new tool or change their daily processes. And with real-time insurance discovery and auto-population of coverage data into Epic, staff no longer needed to rely on guesswork and manual data entry. The tool's ability to automatically determine primacy and remove expired coverage meant staff could submit claims with confidence. "One of the primary reasons we chose Experian and Patient Access Curator was because it makes the manual work of revenue cycle much easier on the registration teams, which in turn improves productivity, empowerment and morale," said Gabel. Outcome When Patient Access Curator went live, the effects were felt almost immediately. Registrars who once spent valuable time debating which checks to run found that PAC handled those decisions automatically, and much faster. Manual searches were no longer necessary, and the system's accuracy drastically reduced the number of errors. These front-end improvements have boosted performance throughout the revenue cycle. Clean registrations meant fewer denied claims, less manual cleanup and faster reimbursements. PAC even uncovered insurance for accounts that had already been sent to collections, helping OhioHealth reduce reliance on contingency vendors and cut avoidable bad debt. PAC continued to prove its value long after it went live. Within the first year, OhioHealth achieved: 42% reduction in overall registration/eligibility-related denials 36% decrease in COB-related denials 69% drop in termed insurance-related denials 63% fewer incorrect payer-related denials $188 million in claims unlocked by reassigning staff and improving productivity What's next? Building on this success, OhioHealth's next steps are to expand their use of PAC by launching a patient financial experience initiative. This will allow patients to complete registration themselves and find their own coverage without waiting for a staff member to become available to help. Resolving more insurance issues upfront will deliver a faster, easier and more transparent registration experience from the start. With Patient Access Curator, OhioHealth has gone from losing time and money dealing with the downstream effects of claims errors to ensuring coverage accuracy at the source – while cutting denials by almost half. Along with a better experience for staff and patients, these gains have created a more resilient revenue cycle, ready to withstand whatever unexpected changes may be in store. Find out more about how Patient Access Curator prevents claim errors before they begin, helping teams submit clean claims and reduce denials. Learn more Contact us

Over the past two decades, U.S. hospitals have absorbed nearly $745 billion in uncompensated care, according to the American Hospital Association. This burden continues to grow as hospitals struggle to verify active insurance. The task is made harder by patients frequently changing jobs, relocating and moving through a fragmented payer system that providers must track and interpret. The result? Missed billing opportunities, delayed payments and unnecessary write-offs threaten not only the hospital's financial stability, but also their ability to provide care to their communities.  Now, the newly enacted "One Big Beautiful Bill Act" adds even more pressure. With sweeping Medicaid cuts and stricter eligibility rules, millions of Americans could lose coverage — and hospitals may face a sharp rise in uncompensated care. Key provisions include:  More frequent eligibility reviews (every six months instead of annually) Higher out-of-pocket costs (up to $35 per doctor visit) New limits on state Medicaid funding (including bans on provider taxes) According to the Congressional Budget Office, an estimated 11.8 million people could lose Medicaid coverage by 2034.  These changes shift more financial responsibility to hospitals and patients.   But the impact isn't just financial. For patients, undetected coverage can lead to surprise bills, postponed treatment, or even collections, all of which erode trust in the healthcare system. Vulnerable populations, particularly those affected by the latest Medicaid changes, are at the greatest risk of falling through the cracks.   Hospitals are committed to serving their communities, including those who may not be able to afford to pay. To do this, they must recover every dollar they're entitled to. That means identifying coverage wherever it exists, even when it’s hidden, forgotten or misclassified.   That’s where Coverage Discovery comes in. Experian Health's solution uses proprietary data and advanced machine learning to identify unknown or forgotten insurance coverage across the entire revenue cycle — before, during, and after care. Unlike traditional eligibility checks, Coverage Discovery goes deeper. It scans commercial, government and third-party payers in real time; it uncovers primary, secondary and even tertiary coverage that might otherwise go unnoticed. This proactive approach helps providers bill the right payer the first time, which reduces denials, accelerates reimbursements, and minimizes bad debt.  Coverage Discovery identified over $60 billion in insurance coverage across 45+ million unique patient cases in 2024 alone, turning missed opportunities into paid claims. In a time of uncertainty, clarity is essential. Coverage Discovery empowers providers to take control of the coverage gap — not just react to it. By surfacing hidden coverage early and often, hospitals can protect their financial health while improving the patient experience.  Here's how it all comes together:  Learn more Contact us

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