All posts by natalie lima

Hospitals that treat Medicaid patients should update their eligibility and billing systems now to prepare for the One Big Beautiful Bill Act (OBBBA), which will bring major changes to Medicaid.

Key takeaways: Providers see eligibility verification and patient access as top use cases, but are cautious about using AI for critical decision-making. Privacy, security, accuracy and cost are seen as the greatest barriers to AI adoption. Most providers expect AI use to keep growing, but agree that some level of human oversight will still be important. New statistics shared by Experian Health show that confidence in AI is growing steadily among healthcare providers, though many remain cautious about how and where it’s used. While 63% of providers have already introduced AI into their RCM workflows in some way, most reserve it for lower-risk tasks such as analysis and automation. Many are still testing the waters to see where AI can add value without compromising accuracy or security. Experian Health surveyed 200 healthcare decision-makers in October 2025 to gauge their feelings about AI. This article summarizes their views on AI adoption in healthcare, including the main barriers, top revenue cycle use cases, and predictions for the next few years. Infographic: The evolving role of AI in healthcare RCM While full trust in AI remains limited, especially for high-stakes decision-making, confidence is rising. How much do healthcare providers trust AI? Experian Health’s data suggests that providers are split between feeling confident about using AI and cautious about letting the technology make decisions on its own. Around four in ten survey respondents say they mostly or completely trust the technology. Three in ten describe their level of trust as moderate, while the remaining third say they trust it only slightly or not at all. These trust levels inform the type of tasks AI is used for. Most providers are comfortable using AI for automation and data analysis, but are hesitant to rely on it for higher-stakes decisions. Only 5% say they would trust AI to make critical decisions independently. Interestingly, hospitals appear to be more confident: half of this group say they mostly or completely trust AI, compared with 28% of other provider organizations. In a recent consultation response, the American Hospital Association stated that hospitals are already seeing AI make a “significant positive impact” in clinical care, noting that “AI tools hold tremendous potential in helping transform care delivery and address some of the administrative burdens that increase costs.” This experience may explain why they’re more comfortable moving ahead with AI in business operations, too. What are the biggest barriers to AI adoption in healthcare revenue cycle management? Concerns about data privacy and security are the top barrier to AI adoption, mentioned by half of the survey respondents. For 41%, accuracy is a sticking point, making it difficult to fully trust AI’s results. Although hospitals tend to be more confident in AI overall, their reasons for hesitating differ from other providers. Hospitals are more apprehensive about regulatory issues, with 26% listing this in their top three concerns compared with 21% of other organizations. On the flip side, they may have found more cost-effective ways to implement AI. Only 23% of hospitals see cost as a barrier compared with 39% of other providers. What areas of the revenue cycle would benefit most from AI? Catching errors early in the revenue cycle According to Experian Health’s data, providers think AI has its greatest impact at the front end of the revenue cycle. More than half (52%) put insurance eligibility and benefits verification in the top three opportunities. Patient scheduling and access follow at 45%, and 44% point to patient registration and data collection. Improving front-end processes is exactly what Patient Access Curator™ (PAC) is designed to do. Using AI and machine learning, it automatically verifies and updates patient insurance information with a single click. Improving data quality at this early stage reduces downstream errors and delays. Clarissa Riggins, Chief Product Officer at Experian Health, discussed the benefits of applying AI early in the revenue cycle in an interview with Medical Economics: “Much of [the denials burden] still comes down to friction in workflows and incomplete or inaccurate registration information at the point of entry,” she says. “Fix the problems at the start and that should address the claim situation toward the end.” Predicting and reducing denials Only 32% of survey respondents reported seeing claims submission and denial prevention as top opportunities for AI. This is somewhat surprising, given that 69% of those using AI reported seeing a reduction in denials and improved resubmission results, according to Experian Health’s State of Claims 2025 survey. This suggests there may be some untapped potential for AI in denial management. Tools like Experian Health's AI Advantage™ utilize advanced analytics and machine learning to identify denial risks earlier, predict outcomes more accurately, and recover revenue more efficiently, functioning as a complement to Patient Access Curator. See how AI Advantage predicts and prevents denials: How are healthcare organizations currently using AI? Currently, nearly two-thirds of providers say they are using AI in some way. Around 15% of providers have fully integrated it into their RCM operations. At the other end of the spectrum, 24% are still in the exploratory stages. For organizations that are not quite there yet, the following resources show the results that are possible: Case study: Experian Health & OhioHealth See how OhioHealth cut denials by 42% with Patient Access Curator by solving claim errors at the source. On-Demand webinar: Reimagining patient access Learn how AI and automation eliminate manual errors, reduce denials and unlock millions in recoverable revenue. How do providers see AI usage changing over the next few years? Most healthcare leaders expect AI adoption to keep growing over the next three to five years. For more than half, this comes with the caveat that human oversight will remain essential. A small number (6%) think progress could stall because of regulatory or trust issues. As adoption increases, providers will need to figure out the balance between autonomy and oversight. The most effective models will see AI and staff working together, with technology improving efficiency and giving teams more capacity to handle those complex, higher-stakes tasks. FAQs Where should healthcare organizations start if they are new to using AI in the revenue cycle? Clarissa Riggins recommends starting small by targeting specific processes where automation can make an immediate difference, such as eligibility verification or claim edits. Running an AI pilot in workflow is a good way to help teams build confidence in the technology and measure results before scaling up. Will AI replace staff in revenue cycle management? Experian Health’s data suggest providers see AI as a way to support, not replace, their teams. By taking on repetitive, data-heavy tasks, AI gives staff more time to focus on problem solving and higher-value work that requires human judgement. How can providers use AI responsibly while maintaining oversight? Successful adoption depends on clear governance, reliable data and staff-friendly interfaces. This means choosing tools that complement human decision-making and enhance oversight rather than remove it. Find out more about how Patient Access Curator and AI Advantage help providers use AI to drive stronger revenue cycle performance. Learn more Contact us

Manual insurance eligibility checks are slow, error-prone and a leading cause of claim denials. Find out how automated insurance verification delivers real-time accuracy, fewer billing errors and faster reimbursements — helping providers protect revenue and improve patient care.

Experian Health’s 2025 High-Performance Summit was a catalyst for collaboration, innovation and a shared commitment to simplifying healthcare – for both providers and the patients that they serve.

To meet evolving price transparency regulations, University of Tennessee Medical Center (UTMC) partnered with Experian Health to implement Patient Estimates. This integrated solution helped UTMC maintain compliance, increase estimate delivery and empower patients with clearer cost breakdowns.

AI is reshaping patient access by reducing manual errors and preventing costly claim denials. Tools driven by AI and automation can streamline eligibility checks and coordination of benefits — helping providers improve efficiency, cut costs, and deliver a better patient experience.

Propensity-to-pay models forecast which patients are likely to pay their bills, enabling smarter prioritization. By using data-driven automation, these tools help healthcare providers boost collections, reduce bad debt and improve overall revenue cycle efficiency.

Claim denials are increasing, putting pressure on staff and revenue. Experian Health's latest report outlines key factors driving denials today and how AI and automation can help providers strengthen claim accuracy and financial performance.

“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