Reducing claims denials to get paid faster

by Experian Health 4 min read January 13, 2022

reducing-claims-denials-to-get-paid-faster

Claims denials are a major source of headaches for healthcare organizations. On average, denied claims can take more than two weeks longer to pay out than first-time claims, if they get paid at all. Denials can have major downstream impacts, including lower annual net revenue, additional hours spent on administrative work, and potential disruptions to patient care.

Claims denials aren’t just an occasional inconvenience, either. A recent American Hospital Association (AHA) survey found that 89% of all hospitals and health systems have seen a rise in denials over the past three years, with half of the participants describing the increase as “significant.”

Data from Healthcare.gov confirms this trend. The Kaiser Family Foundation (KFF) states that in 2019, health plans available on the individual market denied an average of 17% of all claims – up from 14% the year before. It’s becoming more critical than ever for healthcare organizations to employ integrated, intuitive, and technology-driven strategies to get their claims paid in a timely and efficient manner.

Reduce claim denials by eliminating administrative errors and manual processes

Health plans can deny claims for any number of reasons. The good news is that in 2019, KFF found that less than 1% of claims were denied based on medical necessity. The bad news is that the remaining 99% were denied largely due to other reasons. This included referrals, prior authorizations, coverage disputes, data errors, and clearinghouse problems.

Many claims denial issues occur when organizations rely on manual processing of complex documents that are subject to ever-changing requirements from a wide variety of payers. Mistakes are not uncommon, and that ends up costing time and money. Smart, intuitive claims management workflows that take advantage of automation technology can augment staff resources and reduce the likelihood of errors.

Automation contributes to clean and accurate claims the first time around. According to the Council for Affordable Quality Healthcare (CAQH), manual processing can take an average of four minutes per claim. Automation cuts this time by 25%, bringing the total time per claim down to just three minutes.

This might not seem like a lot in isolation; however, it becomes more material when the time savings is applied to a large, multi-hospital health system that partnered with Experian Health to revamp its claims processes. The health system gets through 200,000 claims per month. That could translate into 200,000 minutes saved – or more than 3,300 hours – every 30 days. Amidst the staffing shortages that are persistent in healthcare, those numbers are significant.

For providers of all sizes, the right automation tools use an expansive library of national payer edits, supplemented by custom edits, to ensure that claims are clean before they get out the door. These tools can also organize and prioritize accounts to help staff members use their time most efficiently.

If a claim does have an issue, organizations can use additional technologies to stay one step ahead of the denials process. Enhanced claim status monitoring can give providers insight into potential problems long before the ERA and Explanation of Benefits are processed. This allows organizations to address known issues and predict their revenue cycle outcomes earlier and more accurately.

Automation can also help providers slash even more time off the claims management process. The Council for Affordable Quality Healthcare (CAQH) estimates that it takes between 14 and 30 minutes to complete a manual claims status inquiry. Automated status monitoring can potentially shave 9 minutes off this task, freeing up staff to complete other tasks.

There’s still plenty of options when claims do get denied. Providers can complement their claims capabilities with denials workflow management tools that can generate customized worklists, highlight ANSI reason codes and payer proprietary codes, and identify payer-specific denial trends to help inform decision-making.

Automation creates a faster, more accurate claims processing ecosystem

Most healthcare organizations use a number of different technologies to manage their revenue cycles, and all these systems must work together in harmony. Unfortunately, interoperability across disparate clinical and financial systems isn’t easy to achieve.

In the case of the provider that chose Experian Health to improve its claims process, integration with Epic, its medical records system of choice, was very important. For example, ClaimSource easily loaded customized edits and the edits library into Epic, tracked and corrected claims, and found and repaired issues with the system build, creating opportunities for cross-training and centralized reporting.

Thanks to this automated, integrated process, this provider improved its acceptance rate by 10 percent, consistently seeing 99 percent of its claims accepted. Additionally, its clean, paid claims percentage increased by over 10 percent, creating a more predictable, profitable revenue cycle.

With denials on the rise in an increasingly challenging claims management environment, providers will benefit from replacing manual processes wherever possible.  Automation is the key to optimizing staff resources and significantly reducing reimbursement obstacles.

To see sustained success with your revenue cycle, get in touch with Experian Health and start automating your claims process today.

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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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