AI Revolution in Healthcare: Providers Race to Combat Rising Claim Denials
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As artificial intelligence transforms the healthcare landscape, providers are increasingly turning to AI-powered solutions to navigate rising claim denials and maintain financial stability.
Artificial intelligence (AI) is rapidly reshaping healthcare operations, yet adoption remains uneven despite widespread belief in its potential. A critical challenge for providers is keeping pace with payers who are leveraging AI to control costs, leading to a surge in claim denials and escalating data quality issues. Innovative solutions like Experian Health’s AI Advantage™ and Patient Access Curator™ are emerging as vital tools to help providers prevent denials, improve efficiency, and bolster financial performance.
The Expanding Role of AI in Healthcare
AI is no longer a futuristic concept; it’s actively transforming every facet of healthcare. In clinical settings, AI supports faster and more accurate diagnoses and treatment decisions. At the front desk, it streamlines coverage verification and appointment scheduling. Perhaps most significantly, AI’s ability to analyze vast datasets is revolutionizing how healthcare claims are reviewed, processed, and paid.
However, this transformation isn’t without hurdles. As providers adopt artificial intelligence and machine learning (ML) to enhance care and operations, payers are simultaneously employing the same technologies to manage costs and accelerate coverage determinations. According to the American Medical Association, 61% of physicians report that AI is contributing to an increase in prior authorization denials. Strategic implementation of AI is now essential for providers to remain competitive.
Understanding AI and Machine Learning
Artificial intelligence refers to technology capable of performing tasks that typically require human intelligence, such as pattern recognition, data interpretation, and problem-solving. It learns from experience, identifies trends that might be missed by the human eye, and generates recommendations tailored to user objectives.
Machine learning is a subset of AI that continuously improves its performance over time. This allows healthcare organizations to convert complex data into actionable insights. Because these models are trained on an organization’s unique data, they can adapt to local patterns and workflow variations, resulting in more precise predictions.
Clinical and Operational Applications of AI
AI and ML are finding applications across a broad spectrum of clinical and operational areas. Clinically, they are:
- Improving diagnosis by analyzing medical images with greater accuracy.
- Accelerating drug discovery by tracking side effects and treatment outcomes.
- Enhancing surgical safety and precision through robotics.
- Empowering patients to manage their health through wearables and remote monitoring.
Operationally, AI is enabling staff to work more efficiently. Patient access teams are utilizing AI to verify insurance, forecast demand, and optimize scheduling, while revenue cycle leaders are leveraging it to reduce manual tasks and improve claim accuracy. Experian Health’s State of Claims 2025 report revealed that 69% of organizations using AI solutions have experienced fewer denials or higher resubmission success rates, demonstrating tangible gains in both efficiency and financial performance.
Combating Rising Claim Denials with AI
Despite these advancements, claim denials continue to rise, highlighting persistent issues with data quality. The State of Claims report indicates that over half of providers (54%) are seeing an increase in claim errors, and 68% find submitting clean claims more challenging than the previous year.
The challenge is compounded by payers’ increasing reliance on AI. As American Medical Association President Bruce A. Scott noted, “emerging evidence shows that insurers use automated decision-making systems to create systematic batch denials with little or no human review.”
In contrast, approximately 90% of denials on the provider side require manual rework, creating a widening technology gap that slows reimbursement and strains already burdened teams. AI-based solutions from Experian Health are designed to bridge this gap.
Streamlining Front-End Accuracy with Patient Access Curator
Patient Access Curator utilizes AI and machine learning to automate front-end eligibility and authorization workflows. By verifying eligibility, insurance coverage, and reducing data errors, it helps organizations submit cleaner claims, minimize delays, and enhance the patient experience.
Predicting and Preventing Denials with AI Advantage
AI Advantage employs a two-pronged approach to mitigate denial risk and expedite rework. A recent webinar featuring Eric Eckhart of Community Regional Medical (Fresno) and Skylar Earley of Schneck Medical Center showcased how AI Advantage enabled them to optimize their denials management strategy and maximize reimbursement.
“What really sold [AI Advantage] for me was that it’s looking at my data. It’s not looking at Skylar’s data in the Midwest. It’s looking at my data in central California. We have lots of little payers that do their own thing, and it’s learning from my information, my actual denials that are happening. If the payer shifts, the model’s going to follow that and let me know about it,” explained Eric Eckhart, Director of Patient Financial Services at Community Medical Centers.
Specifically, AI Advantage offers:
- AI Advantage – Predictive Denials: This feature examines claims before submission, calculating the probability of denial based on historical payment data and undocumented payer behavior in real-time. High-risk claims can be edited before submission to reduce denial rates.
- AI Advantage – Denial Triage: This component evaluates and segments denials based on the likelihood of reimbursement, prioritizing the work queue accordingly. It learns from past decisions to refine recommendations, enabling staff to focus on denials with the highest potential for successful resolution.
Implementing AI: Key Considerations
While confidence in AI is high, adoption rates remain relatively low. The State of Claims survey found that 67% of providers believe AI can improve the claims process, yet only 14% currently use it to reduce denials. This suggests caution surrounding the practicalities of AI implementation.
To ensure smooth implementation, providers should prioritize:
- Data quality: AI tools are only as effective as the data they analyze. Partnering with a reliable third-party vendor can help ensure data accuracy and usability.
- Integration: New tools must seamlessly integrate with existing workflows and systems. A single-vendor solution, such as the compatibility between AI Advantage and ClaimSource®, can mitigate interoperability issues.
- Compliance and security: Solutions must adhere to data privacy and security regulations, like HIPAA, to protect patient trust and avoid legal and reputational risks.
As predictive analytics, natural language processing, and automation continue to advance, providers who strategically embrace AI will achieve greater efficiency and faster reimbursements. With payers and competitors accelerating their AI adoption, understanding where and how to apply these tools will be crucial for maintaining adaptability and financial resilience.
See how AI Advantage and Patient Access Curator are helping Experian Health’s clients transform healthcare operations.
