Claims technology as a game-changer for efficiency

by Experian Health 6 min read April 3, 2024

Claims-technology-as-a-game-changer-for-efficiency

Claims denials are a thorn in the side of any healthcare organization. Even with claims denial mitigation tools and processes in place, denials are growing. In Experian Health’s State of Claims 2022 report, 30 percent of respondents said denials increased between 10% –15% annually. To combat rising denials, ensure faster reimbursements, and improve the revenue cycle, healthcare providers need new claims technology that focuses on efficiency.

In this post, learn about the common challenges in traditional claims processing and how to implement automated or AI-based claims management technology to drive healthcare revenue cycle efficiency.

Challenges in traditional claims processing

When it comes to reimbursement, the odds of being paid do not always favor the healthcare provider. The complexity of claims makes for labor-intensive workflows in traditional reimbursement processing. Data is often culled from multiple systems, including electronic health records (EHRs), paper files, diagnoses, test results, insurance verification, and more. Providers lacking a streamlined set of workflows supported by claims technology, experience errors that can lead to denied claims. Three of the most common challenges in traditional claims processing include missing or incomplete claims information, payer-related problems, and a need for more staff, which slows down processing productivity.

1. Missing or incomplete claim information

Missing data is also a huge issue in traditional claims processing. In fact, missing or incomplete data is one of the top reasons for claims denials, particularly in the area of prior authorization. These mistakes often begin upstream at the first point of patient contact and, if not corrected, snowball toward the inevitable denial. Compounding the problem is that disparate healthcare systems and workflows make it increasingly challenging to collect all the data effectively. The larger the healthcare provider, the more touchpoints for claims processing, creating back-and-forth workflows that can lead to miscommunication or the loss of information.

2. Payer-related challenges

Just keeping up with changes in payer requirements is a full-time job. Payers often change reimbursement requirements, and providers aren’t aware of these new adjudication rules. It requires strict monitoring of all payers, which is impossible for organizations to manage. Prior authorizations are also increasingly burdensome for providers to handle. An AMA survey found that 88 percent of physicians said these burdens were high or extremely high. Providers estimated they process 45 prior authorizations weekly, equivalent to 14 hours of staff time.

3. Reduced or new staff can’t keep pace

Another challenge is not having the workforce necessary to review claims to identify errors. Workforce shortages continue to impact every healthcare area. The chronic challenge of high workloads and short staffing means most teams work as quickly as possible, leading to preventable mistakes. Without advanced claim technology, staff manually handle heavy workloads, which is driving denials through the roof.

The lack of staff also affects traditional claims processing by slowing denials resubmissions. A less efficient denials management process directly affects provider cash flow, creating more delays in getting paid.

Resolving these challenges requires modern, advanced claims technology powered by automation and artificial intelligence (AI). By leveraging this technology for claims management, healthcare providers can solve these problems for greater reimbursement efficiency and a better bottom line.

Best practices for implementing AI-based claims management technology

Experian Health data shows 51% of healthcare providers currently leverage some software automation. However, only 11% had integrated AI technology into their organization.

Mounting evidence suggests preventing healthcare claims denials starts with innovative AI-driven claims management technology. AI and automation applied to a claim technology solution can prevent claims denials on the front-end of the patient encounter and improve denial management on the back-end of the process.

When evaluating how to implement advanced claim technology, consider these best practices:

  • Start by identifying the pain points in existing claims processing workflows. Review claims denials and mitigation data and talk with existing staff to develop this list. If the organization leverages legacy reimbursement tools, consider how efficiency gaps affect the organization.
  • Consider organizational goals and objectives for replacing manual workflows or upgrading legacy claims management technology.
  • As the organization explores the benefits of advanced claim technology featuring AI, develop use cases for employing these tools for more effective claims management. Compare new product features to these real-life scenarios.
  • Seek stakeholder feedback. All technology rollouts require significant buy-in at every level in the organization. Don’t miss engaging with the boots-on-the-ground workforce using the claims technology
  • Ensure the organization has the infrastructure to support the new platform long after it goes live.

When evaluating new digital tools, keep these things in mind:

AI technology is the game-changer for healthcare’s skyrocketing claim denial challenges. These new tools deliver immediate value to an increasingly disjointed and complex reimbursement process. With the right technology, healthcare providers improve the claims processing efficiency to get paid faster.

Transformative impact of Experian Health’s advanced claims technology

Experian Health is a leader in digitally transforming traditional claims processing. AI-powered technology can increase staff efficiency at every stage of the claims management process.

Experian Health’s AI Advantage™, part of the Best in KLAS ClaimSource® platform, is transforming provider claims processing. This software reduces the need for additional staff by automating manual tasks. It lessens the burden on existing teams by lightening their claims processing and denials management workloads. AI Advantage has two primary solutions affecting every stage of the claims management process:

  • Predictive Denials identify undocumented payer rules resulting in new denials. This AI-driven solution finds the claims most likely to fail, flagging them back to payment processing for correction before they’re even submitted to the payer.
  • Denial Triage manages prioritization of denied claims. Advanced algorithms in this solution identify and flag denials based on their potential value. Organizations maximize their returns on denied claims by focusing on the resubmissions with the highest financial impact. It removes the guesswork from reworking claims, lessening staff workloads by eliminating time wasted on low-value cases.

Another solution, Patient Access Curator, uses AI and robotic process automation to enable healthcare staff to capture all patient data at registration, with a single click solution that returns multiple results – all in 30 seconds.

Experian Health’s automated and AI-fueled advanced claim technology improves provider reimbursement efficiency at every stage of the process. The efficiency-related benefits of AI for claims management include avoiding denials, accelerating denial mitigation, and getting paid faster. To explore these tools—and their extraordinary ROI, contact the Experian Health team today.

Related Posts

Andy’s New WP Workflow Test Article Using Quick Edit

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.

October 2, 2026 by Andy.Monte@experian.com
Experian Health ranked #1 in Best in KLAS for 2025

Experian Health is very pleased to announce that we've ranked #1 in the 2025 Best in KLAS: Software & Services report, for our Contract Manager and Contract Analysis product, for the third consecutive year. Contract Manager, when paired with Contract Analysis, empowers healthcare providers by ensuring payers comply with contract terms, identifying and recovering underpayments, and arming them with real claims data to negotiate contracts. This enables providers to negotiate more favorable terms and maintain financial stability.  Clarissa Riggins, Chief Product Officer at Experian Health, says, “In the ever-evolving healthcare landscape, our Contract Manager solution has once again been recognized as the #1 Revenue Cycle Management tool by KLAS for the third consecutive year. This prestigious ranking underscores the significant value our solution delivers to our clients by identifying underpayments and facilitating revenue recovery. We are honored to continue supporting our clients with innovative solutions that drive financial success and operational efficiency.”  Learn more about how Contract Manager and Contract Analysis can help your healthcare organization validate reimbursement accuracy, recover underpayments and boost revenue.   Learn more Contact us

February 5, 2025 by kelly.nguyen
How to increase patient engagement

Learn how providers can increase patient engagement, why it matters and key strategies that deliver improved end-to-end patient experiences.

January 30, 2025 by Experian Health

Spotlight test

Spotlight Description

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Sticky Subscribe Title

Sticky Subscribe Description
Sticky Subscribe

Testing Spotlight Paragraph block

Testing the spotlight block header

Archive Testing

Categories