Data accuracy in healthcare: Why it matters for claims

Key takeaways:

• The survey results show mixed progress: 32% of providers report a decrease in denial rates, while the majority say rates have stayed the same or increased, despite confidence in prevention processes.
• The majority of preventable claim denial errors are due to upfront data issues, such as missing documentation, coding errors, and eligibility or authorization issues, highlighting the need for more robust, integrated workflows.
• Vendors see automation and artificial intelligence (AI) as essential to improving front-end accuracy. Experian Health Patient Access Curator™ (PAC) is designed to reduce manual work and avoid denials before claims are submitted.

New research suggests that some healthcare providers are having greater success than others in reducing claim denials. Experian Health Claims Denial Management SurveyConducted in January and February 2026, it asked 210 revenue cycle leaders for their opinion on data accuracy in healthcare. For 32%, claim denials are finally decreasing. But 42% have seen little change in their denial rates over the past year, and 25% have seen their rates continue to increase.

Ash increasing costs and OBBBA Impacts As we push providers to find better ways to handle denials, many will wonder: what are high performers doing differently?

According to the survey results, the answer lies in improving the accuracy of the initial data. This article summarizes key insights into common root causes of denials and where providers should focus their denial prevention efforts.

Why is data accuracy important in healthcare?

Healthcare is based on accurate data. When information is missing or incorrect, providers cannot confidently coordinate care, manage operations, or secure reimbursement. Because healthcare systems are interconnected, a single inaccurate piece of information can contaminate entire patient records and disrupt workflows across departments.

This is especially visible in the revenue cycle. Many claim denials are due to small data errors entered long before the claim is submitted, which subsequently derail reimbursement. Getting patient details and coverage right from the start makes it much more likely that claims will be 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. About two-thirds consider themselves 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 to be ineffective in these areas.

But if most providers are confident in their prevention processes, why aren’t rejection rates decreasing more consistently? One explanation is that individual performance metrics may appear healthy, but are not always linked to actual denial outcomes. Patient access, coding, and payment compliance can work well in isolation, but must be integrated as a single system to break the spiral of denial.

Additionally, increasing external pressures, such as payer scrutiny and changes in coverage, could offset internal improvements. In a more challenging payer environment, “effective” may not be enough.

Where are the biggest opportunities to reduce claim denials?

When asked what they see as the biggest opportunities to reduce denials, 50% of respondents He included front-end accuracy in his top three.

Staff training and accountability came in second, cited by 42% of respondents. This suggests that organizations view improvement as a matter of workflow and consistency as much as a technological challenge. About a third of respondents selected improvements in coding and charge capture (39%), improved analytics and reporting (34%), and better clinical documentation support (33%) as the path to fewer denials.

These responses indicate a growing interest in laying the groundwork for cleaner claims. Providers are looking to improve how data is captured, validated and monitored throughout the revenue cycle so they can prevent errors sooner.

What are the most common and avoidable claim denial errors?

Providers were also asked what is currently going wrong in claims management. Their responses highlight the importance of data accuracy in healthcare.

The top three most avoidable causes of claim denial were:

1. Missing or incomplete documentation (cited by 53% of respondents)

2. Coding errors (45%)

3. Duplicate claims (39%)

Other common problems included eligibility and coverage errors (35%), services not covered (33%), authorization not obtained or expired (30%), and late submission (30%). The fact that these problems are widely considered avoidable 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 avoidable errors related to documentation, eligibility and authorization, it is not surprising that providers see automation as a key part of the solution. Revenue Cycle Automation It can process large volumes of data in seconds and compare and verify information between systems to detect errors before they cause damage. Automated tools can also handle repetitive tasks based on rules, allowing teams to focus on higher-value work.

When asked where automation could have the biggest impact, respondents again pointed to the first steps in the revenue cycle:

1. Front-end automation for registration and verification ranked highest, selected by 49% of respondents.

2. 45% cited coding validation and clinical documentation support.

3. Automated authorization checks and alerts were considered a sensible use of automation by 43%.

Respondents also identified AI-powered denial prediction and prevention (35%), real-time compliance with payer policies and rules (33%), automated appeals workflows (27%), and automated claims purging (23%) as important areas for investment.

Patient Access Curator puts this into practice by using artificial intelligence and automation to help teams get the right log data the first time. Instead of relying on staff to jump between multiple systems to manually verify patient information, it brings together demographic data, eligibility verifications, benefits coordination, 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/opportunity denials

PAC detects data issues early and information is accurate before a claim is created. Registrars no longer need to make complex and painful decisions that can lead to rework and denials.

Cleaner claims start from the beginning

The survey results make it clear that reducing denials begins with reducing front-end data problem and improve data accuracy. Getting patient and coverage data up front 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.

Frequently asked questions

Learn more about how Experian Health Patient Access Curator Helps healthcare organizations improve baseline data accuracy and reduce claim denials.