Claims management process strategies for claims acceptance success

by Experian Health 6 min read January 31, 2024

Claims management process strategies for claims acceptance success

Healthcare is a challenging profession. Providers understand that their mission of care delivery is fueled by the revenues they capture; after all, it is the business of healthcare. However, capturing revenues through the claims management process is burdensome and complex. Denials are all too common, hampered by inefficient workflows and manual tasks. As a result, it slows down reimbursement and impacts revenue. Moving toward reliable claim acceptance requires the strategic use of automation and technology to reduce denials. These initiatives accelerate the cycle of payments, improve cash flow, and ease strains on existing staff. This article takes a deep dive into the challenges of healthcare claims processing and strategies to help providers transform the claims management process.

Challenges of healthcare claims processing

The healthcare claims management process desperately needs modernization and optimization. Last year, an Experian Health survey showed that three out of four providers say reducing claims denials is their top priority. What’s making it so difficult for providers to get paid?

The healthcare reimbursement journey

Let’s start with the typical claims management process.

Step 1: Prior authorization

The first issue is that most generally accepted standard practices in healthcare claims processing create a long journey for provider reimbursements. This journey starts even before patient care, at eligibility and preauthorization. The American Medical Association (AMA) states, “Prior authorization is a huge administrative burden for physician practices that often delays patient care.” While prior authorization may help insurance companies reduce the cost of “unnecessary” treatments, the data shows it’s having the opposite effect on the providers themselves. An AMA physician survey shows that 86% of prior authorizations lead to higher overall utilization of services. The practice doesn’t appear to help patients, either; the AMA says 94% of doctors report care delays related to prior authorization, and 82% say patients abandon their treatment plans due to prior authorization struggles.

Step 2: Data capture

The second part of healthcare claims processing begins after the patient encounter. It involves many manual tasks, often leading to errors and claim denials. Intake and billing specialists must gather data from multiple sources for coding claims, including electronic health records (EHRs), physician notes, diagnosis codes, paper files, and the patients themselves. These workflows require significant manual data entry and review, which is impacted further when codes or insurance reimbursement requirements are out-of-date in provider systems. A recent survey shows that 42% of providers report code inaccuracies, and 33% say missing or inaccurate claims data as the top reasons for rejected claims.

Step 3: Processing claims denials

Post-submittal, there’s more work when claims bounce back. It’s part of the claims management process with the most inefficiencies and friction, costing the average provider millions annually. Healthcare providers experience Experian Health survey—but that number is rising. Responses from Experian Health’s State of Claims 2022 report revealed that 30% of respondents experience denials increases of 10 to 15% annually.

In June 2022, Experian Health surveyed 200 revenue cycle decision-makers to understand the current state of claims management. Watch the video to see the results:

These challenges illustrate the need for modern and optimized healthcare claims processing. With this lens in place, healthcare providers can apply more effective claims management strategies to increase claims accuracy and reimbursement and reduce denials.

Innovating your claims management strategy

Healthcare professionals and organizations can proactively address challenges in the claims acceptance process by implementing effective strategies to optimize revenue cycle management. This effort should include the following:

  • A cohesive and comprehensive claims management process
    New approaches to outdated claims management workflows will address gaps, inefficiencies, and errors. Upgrading to a turnkey insurance claims manager can reduce denials and speed up claims processing.
  • Address data quality and consolidation
    The sheer volume of data required for healthcare claims processing increases the risk of errors. If the data isn’t accurate at the front end, it’s a fast track to denial. But claims go through multiple touch points in disparate systems without a single source of control and oversight. Organizations can employ standards for data intake to reduce inaccurate or incomplete patient information and duplicates and leverage technology to aggregate data from the multiple sources needed for claims processing.
  • Implement best practices for denial workflows
    Claim denial management on the backend of healthcare claims processing is even more challenging than capturing patient data at the front end of the encounter. Managing claims denials is time-consuming, and delays reimbursements, but denial workflow technology can streamline all follow-up activities. With this support, billers have less administrative work and can stretch farther, alleviating the burden of staffing shortages.
  • Deploy tools for analysis and prioritization
    A claims management platform can automatically analyze the components of each claim. With this information, the technology can prioritize denials workloads so high-impact accounts get the most attention.
  • Upgrade claims technology automation with artificial intelligence (AI)
    Providers can transform claims management with a technology update. According to the State of Claims report, almost half of organizations replaced legacy healthcare claims processing technology in the past year. A vital component of this upgrade includes expanded automation capabilities that stretch the workforce further. Solutions like AI Advantage™ can help speed up the claims management process by predicting and preventing denials.
  • Add prior authorization software
    Analysis suggests that healthcare could automate up to 33% of manual tasks. Research on the benefits of automation showcases its potential for decreasing errors and other reimbursement obstacles. With prior authorization software, task assignment is seamless, and AI adds even more functionality with predictive capabilities.
  • Accelerate claim follow up
    Monitoring claim statusis another aspect of the payment ecosystem that heavily impacts provider cash flow. Technology automates much of this workflow. Organizations can adopt functionality that eliminates manual follow-up tasks to accelerate an unwieldy process. These solutions enable providers to respond quickly to issues, enhancing productivity beyond basic ANSI 277 claims status responses.

Technology is the unifying thread behind a cohesive claims management strategy for any healthcare provider struggling with a high rate of denials. While 61% of providers lack automation in the claims/denials process, increasing evidence shows these tools drive revenue cycle efficiencies that transform claims denials management. Forward-thinking organizations like Summit Medical Group Oregon—Bend Memorial Clinic (BMC) leverage Enhanced Claim Status and Claim Scrubber to achieve a 92% primary clean claims rate. Schneck Medical Center uses AI Advantage to denials by an average of 4.6% each month.

Implementing effective claims management strategies

Strategies rooted in reliable, practical technology transform the claims management process. Healthcare organizations benefit from AI-driven automation solutions as part of an overarching claims management strategy that streamlines workflows, reduces denials, and boosts cash flow. Experian Health offers a portfolio of provider claims management tools to help organizations realize effective claims management process improvements to get paid faster.

Learn more about the No. 1 Best in KLAS 2023 Claims Management and Clearinghouse tools or contact us to see how Experian Health can help improve your claims management processes.

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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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