At A Glance
Experian Health’s new denial management survey shows that preventable errors at registration continue to drive denials, making front-end data accuracy and automation essential to reduce rework, protect revenue and submit cleaner claims the first time.
Key takeaways:
- Survey results show mixed progress: 32% of providers report declining denial rates, while most say rates have stayed the same or increased, despite confidence in prevention processes.
- Most preventable claim denials errors stem from front-end data issues such as missing documentation, coding mistakes and eligibility or authorization issues, highlighting the need for stronger, more integrated workflows.
- Providers see automation and artificial intelligence (AI) as essential to improving front-end accuracy. Experian Health’s Patient Access Curator™ (PAC) is designed to reduce manual work and prevent denials before claims are submitted.
New research suggests that some healthcare providers are achieving greater success than others in reducing claim denials. Experian Health’s Claim Denial Management Survey, carried out in January and February 2026, asked 210 revenue cycle leaders for their views on data accuracy in healthcare. For 32%, claim denials are finally decreasing. But 42% have seen little change in their denial rates over the last year, and 25% have seen their rates continue to rise.
As growing costs and OBBBA impacts put pressure on providers to find better ways to handle denials, many will be asking: what are the high performers doing differently?
According to the survey findings, the answer lies in improving front-end data accuracy. This article summarizes key insights into common front-end causes of denials and where providers should focus their denial-prevention efforts.
Why is data accuracy important in healthcare?
Healthcare runs on accurate data. When information is missing or incorrect, providers cannot coordinate care, manage operations or secure reimbursement with confidence. Because healthcare systems are interconnected, a single piece of inaccurate data can pollute entire patient records and disrupt workflows across every department.
This is especially visible in the revenue cycle. Many claim denials can be traced back to small data errors introduced long before a claim is submitted, which later derail reimbursement. Capturing patient and coverage details correctly at the start makes it far more likely that claims are clean the first time, so providers can keep services running smoothly.
Focus on claims management
The survey shows that despite uneven improvements in denial rates, most organizations believe they are performing well in their core denial prevention activities. Around two-thirds rate themselves as very or extremely effective at capturing patient demographics and verifying insurance, and more than six in ten say the same about prior authorization, clinical documentation and coding accuracy. Very few consider their organizations ineffective in these areas.
But if the majority of providers feel confident in their prevention processes, why are denial rates not falling more consistently? One explanation is that individual performance metrics may look healthy, but are not always tied to real denial outcomes. Patient access, coding and payer compliance may work well in isolation, but they need to integrate as a single system to break the denial spiral.
In addition, increasing external pressures such as payer scrutiny and coverage changes could offset internal improvements. In a more challenging payer environment, “effective” may not be enough.
Where are the greatest opportunities to reduce claim denials?
When asked what they see as the biggest opportunities to reduce denials, 50% of respondents listed front-end accuracy among their top three.
Staff training and accountability came second, cited by 42% of respondents. This suggests organizations see improvement as a workflow and consistency issue as much as a technology challenge. Around a third of respondents selected improvements to coding and charge capture (39%), enhanced analytics and reporting (34%), and better clinical documentation support (33%) as the route to fewer denials.
These responses indicate a growing focus on building the foundations for cleaner claims. Providers are looking to improve how data is captured, validated and monitored across the revenue cycle so errors can be prevented earlier.
What are the most common and preventable claim denial errors?
Providers were also asked about what’s currently going wrong in claims management. Their responses highlight the importance of data accuracy in healthcare.
| The top three most preventable causes of claim denials were: |
| 1. Missing or incomplete documentation (cited by 53% of respondents) |
| 2. Coding errors (45%) |
| 3. Duplicate claims (39%) |
Other common issues included eligibility and coverage errors (35%), non-covered services (33%), authorization not obtained or expired (30%) and untimely filing (30%). The fact that these issues are widely seen as preventable speaks to the opportunity to improve front-end processes.
Where will automation have the biggest impact when it comes to claim denials?
Given the number of preventable errors tied to documentation, eligibility and authorization, it’s not surprising that providers see automation as a key part of the solution. Revenue cycle automation can process large volumes of data in seconds, and match and verify information between systems to flag errors before they do damage. Automated tools can also handle repetitive, rules-based tasks, allowing teams to focus on higher-value work.
| When asked where automation could have the greatest impact, survey respondents again pointed to the earliest steps in the revenue cycle: |
| 1. Front-end automation for registration and verification ranked highest, selected by 49% of respondents |
| 2. Coding validation and clinical documentation support were cited by 45% |
| 3. Automated authorization checks and alerts were seen as a sensible use of automation for 43% |
Respondents also identified AI-driven denial prediction and prevention (35%), real-time payer rule and policy compliance (33%), automated appeals workflows (27%) and automated claims scrubbing (23%) as important areas for investment.
Patient Access Curator puts this into practice by using AI and automation to help teams get registration data right the first time. Rather than relying on staff to hop between multiple systems to manually check patient information, it brings demographics, eligibility checks, coordination of benefits, Medicare Beneficiary Identifiers and insurance discovery into a single workflow.
| Four Experian Health clients who have been using PAC for six months to a year have seen the following average reductions in denial rates: |
| 1. 45% reduction in registration denials |
| 2. 33% reduction in COB denials |
| 3. 35% reduction in eligibility/timeliness denials |
PAC catches data issues early and the information is accurate before a claim is created. Registrars no longer need to make hurried, complex decisions that can lead to rework and denials.
Cleaner claims start at the front end
The survey findings make clear that reducing denials starts with reducing the front-end data problem and improving data accuracy. Getting patient and coverage data right from the start helps reduce rework, improve cash flow predictability and minimize administrative pressure.
For many providers, automation is seen as a way to scale front-end operations while maintaining or even improving data accuracy, and reducing reliance on manual processes that can introduce inconsistencies and errors.
FAQs
Experian Health’s denial management survey indicates that clean claims begin with patient access. Accurate demographics, eligibility checks, authorizations and coordination of benefits captured at registration help prevent errors that can later lead to denials, rework and delayed reimbursement.
Experian Health’s findings show that AI and automation can verify and match data across systems in real time, flag errors early and reduce repetitive manual tasks. This helps teams submit cleaner claims, prevent avoidable claim denials and improve revenue cycle management.
Experian Health recommends tracking metrics that connect front-end performance to claim outcomes. Useful KPIs include first-pass claim acceptance rate, denial rates by root cause, eligibility and authorization-related denials and rework volumes.
Find out more about how Experian Health’s Patient Access Curator helps healthcare organizations improve front-end data accuracy and reduce claim denials.
