Denial prevention: Why manage denials when you can prevent them?

by Experian Health 6 min read October 16, 2024

denial-prevention-why-manage-denials-when-you-can-prevent-them

The denial challenge is getting tougher. In Experian Health’s latest State of Claims 2024 survey, almost three-quarters of healthcare administrators agree that claim denials are increasing. The majority also agree that difficulties with claims—like reimbursement times, errors, and payer policy changes—are becoming more common. It’s no surprise that denial prevention is a priority for 84% of respondents.

However, many organizations still focus on reactive strategies, like working harder with denial management teams or appealing claims once the denial comes through. These efforts have their place, but they only address the problem after it occurs. It’s a time-consuming, costly and ultimately inefficient way to face the denial challenge overall.

A better approach is to figure out how to prevent claim denials in the first place. This article looks at how to build a proactive denial prevention strategy using automation and artificial intelligence (AI), to streamline claims processing and nip denials in the bud.

Understanding denial prevention in healthcare

Preventing denials starts with understanding the “ins and outs” of the claims process, particularly payer requirements. Denials occur when a payer refuses to reimburse a provider for services rendered, often due to avoidable coding errors, missing documentation or procedural mistakes. When that happens, providers are left to rework the submission or look elsewhere – most likely to the patient – to fill the funding gap. Many are simply written off to bad debt.

To avoid receiving an 835 file with the dreaded claim denial notice, providers must focus on the root causes of denials and get ahead of the pitfalls.

The importance of claim denial prevention

With denial rates exceeding pre-pandemic levels, 42% of survey respondents say the economy and declining consumer confidence make payer reimbursements more urgent.

While financial stability is the obvious driver for getting claims right the first time, denial prevention also improves operational efficiency and reduces the billing and coding staff workload. Denials are frustrating for patients and staff. When claims are processed correctly the first time, providers avoid delays and billing complications and reduce patient stress over unexpected costs. Preventing denials is critical for maintaining trust and ensuring patients feel secure about their financial obligations.

How to prevent claim denials

Denial prevention strategies should start with addressing the underlying causes of denials. Here are five denial prevention strategies to consider:

1. Improve data accuracy from the start

Garbage in, garbage out. If patient information, insurance eligibility, prior authorizations and billing codes are input incorrectly or missing altogether, providers will continue to submit error-filled claims that have no hope of being paid. Tools like Registration Accelerator and Patient Access Curator can verify relevant data for accuracy before claim submission and reduce the risk of denial.

2. Use AI and automation for efficiency

If there was ever a case for using automation and artificial intelligence, it’s in claim denial prevention. However, around half of providers are still using manual processes, leaving them playing catch-up to the payers who are already using AI to work at scale. Only 10% have automated the process, using AI to correct and resubmit claims.Tools likeClaimSource® can automate eligibility verification and coding, perform error checks before submissions and ensure claims meet payer requirements instantly.This cuts the time and effort wasted on manual processes, releasing staff to focus on activities that need human attention.

3. Automate pre-claim scrubbing to catch errors

A great use case for automation is in providing an extra pair of eyes to pore over claims and catch common errors like missing data and wrong codes before submission. Experian Health’s Claim Scrubber analyzes claims line by line to ensure that claims are submitted to payers and clearinghouses without errors, increase first-time pass rates and prevent rebilling.

4. Track performance for ongoing improvement

Every denial prevention strategy should include monitoring and reporting. Tools that offer real-time tracking of key performance indicators such as denial rates, clean claim percentages, resubmission times, and the reasons for denials can help staff identify patterns. With these insights, they’ll have complete visibility into any recurring problems clogging up their claims processes.

5. Outsource to a trusted vendor for extra support and expertise

Finally, providers might consider outsourcing denial prevention to a specialist vendor who can help them develop the right strategy and toolkit to streamline billing, improve data integrity and manage claims to ease pressure on internal resources.

Experian Health was client-rated #1 by Black Book™ ’24 in Denial & Claims Management Outsourcing, Health Systems.

Proactively reducing claim denials

These strategies raise an important question: can existing revenue cycle technology handle the increasing volume of denials? Healthcare administrators aren’t convinced: only 54% of survey respondents feel their organization’s technology is sufficient to meet demand, down 23 percentage points since 2022. To implement these denial prevention strategies effectively, providers may need to consider upgrading their toolkit rather than relying on traditional systems.

Experian Health offers two AI-powered solutions that help providers better predict and prevent denials:

Prevent denials with Patient Access Curator

Too many denials originate in patient access, so prevention must start here. Patient Access Curator uses AI-driven data capture technology to verify patient details quickly and accurately. With a single click, PAC can automatically check eligibility verification, coordination of benefits, Medicare Beneficiary Identifiers, coverage discovery and financial status. Running multiple manual queries is a thing of the past, saving staff hours and propagating clean data throughout the entire revenue cycle.

Watch the webinar to learn more about how Patient Access Curator helps prevent denials with accurate data from the start.

Predict denials with AI AdvantageTM

Clean data sets the stage for denial prevention, but AI adds an extra layer of protection by forecasting potential issues before it’s too late. AI AdvantageTM does this in two ways. First, the Predictive Denials component analyzes claims using the provider’s own ClaimSource® data and alerts staff to high-risk claims so errors, inconsistencies or missing documentation can be corrected before submission. Next, the Denial Triage component prevents missed revenue opportunities by segmenting denials and guiding staff to those worth reworking.

See how AI Advantage works:

If providers can’t prevent denials, they can’t protect their bottom line. With the right data analytics, automation and AI, providers can take control and spot issues before they become problems instead of spinning their wheels in endless rework. With more advanced tech on their side, it’s possible to close the gap with payers and prevent denials, but it also gives staff the headspace to focus on patient care and support.

Find out more about how Experian Health’s Claims Management solutions help providers build effective denial prevention strategies and reduce lost revenue.

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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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