At A Glance
For healthcare providers, claim denials are a constant drain on revenue and staff capacity. Jason Considine, President at Experian Health, sees three ways artificial intelligence (AI) can break this cycle: by preventing avoidable errors, prioritizing high-value resubmissions and using data insights to reduce denials over time.
Key takeaways:
- Claim denials are primarily a data issue, not an appeals problem, making error prevention the biggest area for improvement.
- AI-powered solutions like Patient Access Curator™ (PAC) and Experian Health’s AI Advantage™ can help teams improve upfront accuracy, reduce claim denials and focus resubmission efforts on claims most likely to pay.
- With more reliable data insights, providers can understand why denials are happening and take a proactive approach to denial management.
For many revenue cycle teams, claim denials have become a routine (and painful) cost of doing business, eating up time and money. Frustratingly, much of that burden is avoidable. Too often, staff recognize that including the correct information upfront would have reduced the likelihood of the claim being returned.
According to Jason Considine, President of Experian Health, preventing denials comes down to how well organizations manage data on the front end. Even the smallest errors in registration, eligibility or authorizations can trigger denials and rework.
In a recent article for Healthcare Business Today, Considine shared his observations on how artificial intelligence (AI) is starting to change how providers approach that challenge. This blog post looks at how teams can integrate these strategies into their own revenue cycle operations.
What’s driving high claim denial rates?
Most claim denials are the result of avoidable errors. In Experian Health’s recent State of Claims survey, more than a quarter attribute at least 10% of their denials to inaccurate data collected at patient intake.
“Inaccurate or missing data, authorization mistakes, outdated insurance and incomplete registration are the most common reasons claims are denied.”
Jason Considine, President at Experian Health
The situation is worse for teams that rely on manual checks. Errors are often discovered only after a claim is denied, and fixing them becomes far more expensive and time-consuming. At that point, staff must review the claim, identify the issue, correct it, and resubmit, all while new work continues to accumulate. It’s a lot to ask of staff who are already juggling full task lists.
How does AI prevent errors before claims submission?
The most effective way to reduce denials is to stop errors before claims ever reach a payer. AI-based tools can review large volumes of registration and claims data in real-time to identify inconsistencies more quickly and with greater accuracy than teams using manual processes.
“By leveraging tools with AI, providers can get ahead of the mistakes,” says Considine. “These solutions can review claims data in real time and flag inconsistencies and missing or inaccurate data and ultimately, predict which claims are most likely to be denied before they are submitted.”
Patient Access Curator, Experian Health’s most robust solution, uses AI to improve front-end data collection. PAC consolidates eligibility verification, insurance discovery and demographic data validation, all in one. As a result, fewer errors make it to submission. Staff spend less time chasing avoidable issues and more time on exceptions that need human judgment.
Optimizing resubmissions and reducing staff burnout
Data from 2022 shows that despite multiple rounds of appeals by hospitals and health systems, only 54.3% of denials were overturned, at a cost of almost $20 billion. These costs could be reduced with better prioritization: many teams work denials in order, regardless of the likelihood of a successful appeal. This spreads staff thin and contributes to burnout.
Using AI to reduce healthcare claim denials is more efficient and alleviates the pressure on staff. Considine says that “by prioritizing the claims that are worth the time and effort instead of treating every denied claim as equal, health organizations can produce the best ROI for the team’s efforts.”
A great example of this is Experian Health’s AI Advantage, which uses predictive analytics to identify high-risk claims before submission and route them for correction. It also prioritizes denials based on the likelihood of reimbursement, so staff don’t lose time on unproductive rework. The model gets more effective over time because it continuously learns from and adapts to payer behaviors.
With denials and staffing shortages on the rise, an efficient claims management strategy is essential. Hear from Eric Eckhart of Community Regional Medical (Fresno) and Skylar Earley of Schneck Medical Center as they discuss how they integrated AI tools before claims submission and upon receiving denials.
Utilizing data insights for long-term denial reduction
Although uptake of AI in revenue cycle management is increasing, many providers remain cautious. New data from Experian Health suggests that while 63% have introduced AI into their workflows in some way, most are using it for lower-risk tasks rather than independent decision-making. Using AI to analyze data can be a good way to see value from the technology without going too far beyond those comfort levels.
Considine highlights how AI can help providers better understand why denials occur and where processes are breaking down. “Without understanding the cause of denied claims, it’s hard to prevent them,” he notes. “AI-powered analytics takes away the guesswork.”
By analyzing patterns in large numbers of claims, AI can identify recurring issues tied to registration, authorization, documentation or payer-specific requirements, giving leaders better visibility into where changes are likely to have the greatest impact.
Making AI adoption manageable
Experian Health’s State of Claims survey reveals that 69% of providers utilizing AI have already experienced a reduction in denials. Still, overall adoption remains quite low. Considine says the key is to start small.
“Deploying an AI pilot in a specific area, such as patient registration or resubmissions, allows organizations to see results and develop confidence in the investment.” With support from the right vendor, teams can determine how AI fits into their workflows and demonstrate value before scaling.
For providers under pressure to do more with less, these three AI-driven strategies can help reduce claim denials, break the cycle of rework and create more predictable revenue cycle performance.
FAQs
Claim denial rates are still rising because many denials stem from preventable data and process issues, including missing information, authorization errors and changes in payer requirements. Manual workflows struggle to keep pace, especially where staffing capacity is limited.
AI can help reduce claim denials by identifying and correcting errors before claims are submitted. AI can also help teams prioritize high-value resubmissions and analyze denial patterns, allowing staff to use their time more effectively.
Yes. By preventing avoidable rework and helping teams focus on claims most likely to pay, AI reduces administrative burdens and improves workload balance.
Learn more about how Experian Health’s AI-powered solutions, like Patient Access Curator and AI Advantage, can help providers reduce healthcare claim denials.
