The future of healthcare claims and AI

by Experian Health 6 min read August 9, 2023

Many hospitals and health systems are rethinking their responses to the growing challenge of healthcare claims management. After all, claims are becoming increasingly more complex. Payer policy edits are changing at a scale not seen before. And the legacy of the pandemic continues to take a toll on administrative workflows. In Experian Health’s State of Claims survey 2022, providers reiterated the urgent need to optimize claims management – and the mountains of wasted dollars that are the by-product of preventable denials. Could artificial intelligence (AI) and machine learning (ML) be the key? What does the future of healthcare claims and AI look like?

The internet is buzzing with excitement about the AI revolution, but the adoption of AI technology in healthcare has been slow, compared to other industries. Providers may be unclear about implementing AI effectively or struggle to see a route around barriers to adoption. This includes concerns around legacy systems and data interoperability. That said, the uptake of AI in healthcare shot up by 167% between 2019 and 2021, as organizations spotted opportunities to leverage new technology to reduce denials, optimize processes and identify patterns. Now, the AI genie is out of the bottle. As the trend continues to grow, providers that fail to embrace these technological advances risk falling behind as their competitors race forward.

This article looks at AI’s role in the future of healthcare claims management, and specifically, how it can help providers streamline claims processing, recoup more revenue and gain a competitive edge.

The growing challenge of healthcare claims management

In Experian Health’s State of Claims Survey 2022, providers said reducing denials was their number one priority. It’s clear to see why. There have been more than 100,000 payer policy changes between March 2020 and March 2022. Staffing shortages continue to put pressure on both front-and back-office teams. Increasing patient volumes and changes to insurance coverage means more claims to process – with more complexity to boot.

Looking ahead, providers need to find more efficient ways to manage and utilize increasing volumes of claims data to alleviate staffing pressure, improve productivity and future-proof against unexpected events. Failure to do so could be an expensive mistake, especially when margins are already tight and the economic landscape remains shaky.

Digital claims management: from process-automation to pattern-spotting

The survey suggests providers are increasingly turning to automation to improve claims management, with 78% saying they were likely to replace their current solution to achieve lower denial rates in the coming year. Upgrading claims technology, automating the tracking of payer policy edits, and automating patient portal claims reviews were the top three strategies for reducing denials.

Automation can generate years of ROI by executing repetitive and error-prone administrative tasks at speed and at scale. A few examples of automation in action are tools like:

  • ClaimSource®, which manages the entire claims cycle, creating custom work queues and automating the claims process for greater efficiency and accuracy.
  • Claim Scrubber, which automatically reviews every line of every claim to check for errors, so claims are clean the first time, prior to submission. 
  • Denials Workflow Manager uses automation to help providers eliminate manual processes, prevent errors and increase reimbursement.

AI takes this a step further, by analyzing vast amounts of information to find patterns and make predictions that support better, faster decision-making.

Clarissa Riggins, Chief Product Officer at Experian Health explains why providers should embrace AI in claims and denials management:

“Claims submissions and managing claims after denial are highly manual processes – and they are both extremely error-prone. AI/ML can learn from the data patterns in your claims to provide insights on where your claims are being denied most frequently. These solutions can also provide decision support to staff to help them to prioritize the work within their current claims processes, to avoid unnecessary denials in the first place and then to optimize their work to ensure a cleaner claim rate.”

While many providers see the potential of AI to streamline claims operations, prevent denials and accelerate reimbursement, others are hesitant to invest or are stumped by logistical barriers. Legacy technology, data compatibility issues and staff skills gaps can all put the brakes on AI implementation. But the AI train is showing no signs of slowing, and providers that fail to jump aboard could get left behind. With the right tools and an experienced vendor, implementation can be simplified.

AI Advantage™ – the engine for predictive denials and denials triage

Experian Health’s new AI-powered denials management solution uses a two-pronged approach to predict, prevent and prioritize denials. First, AI Advantage – Predictive Denials identifies claims that may be at risk of being denied, based on analysis of historical payment data and payer decisions. This gives staff time to intervene and make any necessary amendments before the claim is submitted. The second element, AI Advantage – Denial Triage, applies an algorithm to segment denials based on the likelihood of reimbursement. This means staff can focus on high-impact resubmissions, rather than simply prioritizing high-value claims that may or may not be paid.

Rob Strucker, Product SVP at Experian Health, explains that AI Advantage™ is continuously learning in real-time, so that predictions are increasingly accurate: “We look at the provider’s own information for this type of service for this payer, and how those claims have been adjudicated. From that, we can score each claim in terms of its probability of being denied or claimed, and then based on that probability score, trigger an appropriate alert.”

How Schneck Medical Center optimized healthcare claims management with AI Advantage™

AI Advantage™ proved to be the solution Schneck Medical Center was looking for when they set out to reduce denials. Within six months, Experian Health’s AI-powered solution enabled Schneck to reduce denials by an average of 4.6% each month. Staff reported that the probability thresholds calculated by AI Advantage™ were highly accurate, facilitating a more efficient approach to reworking claims. Processing time was cut from 12 to 15 minutes to less than 5 minutes per claim.

Clarissa Riggins says that AI Advantage gives staff confidence that they’re spending their time on the right tasks: “When you have an algorithm that can evaluate the probability that a denial will be overturned, you can make sure that staff are working on the claims with the most potential for yield. Taken together, these solutions can help ensure that hospitals and health systems are getting paid for the good work they do in delivering care.”

Thanks to the tool’s predictive capabilities, staff now have the insights (uncovered from within their own data) to prevent denials before claims are submitted, and to speed up rework should any be denied.

As claim denials continue to increase in number and complexity and healthcare costs continue to grow, providers are feeling the impact on their revenue and margins. AI can ease the pressure by optimizing the healthcare claims management process.

Find out more about how AI Advantage™ can help providers improve healthcare claims management and prevent costly claim denials.

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Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

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