Using AI to Close the Provider Knowledge Gap and Reduce Improper Payments

By Richard Mennuti, MBA, Associate Vice President, Fraud, Waste & Abuse

Introduction

Improperly submitted medical claims cost health care payors billions of dollars annually, while also posing serious compliance, operational, and reputational risks. Despite long-standing investments in post-payment audits, provider training sessions, and fraud detection tools, many payors struggle to proactively reduce these improper payments.

At Constellation Quality Health, we believe the solution lies in moving upstream and leveraging artificial intelligence (AI) to proactively identify and address provider knowledge gaps before a claim is submitted. By analyzing both claim denial patterns and the types of inquiries providers submit, AI can pinpoint areas where confusion or misunderstanding commonly occurs. These insights can then be used to generate targeted micro-trainings that directly address those gaps, improving provider behavior and reducing the risk of improper payments.

AI-powered knowledge gap detection and training can deliver not only reduced denial rates and streamlined payment operations but also foster stronger provider engagement and improve compliance outcomes.

The Problem: Persistent Financial Risk from Improper Claims

Health care payors whether Medicaid agencies, commercial insurers, or managed care organizations, face an ongoing threat to operational efficiency and financial performance due to improperly submitted claims. These errors are often not malicious but stem from:

• Misinterpretation of billing and coding requirements
• Insufficient or incorrect documentation practices
• Lack of ongoing training or updates on coverage policies
• High staff turnover and varied experience levels at provider sites

The cost of these recurring issues includes:

• Repetitive administrative rework, such as appeals and resubmissions
• Delays in provider reimbursement, straining network relationships
• Heightened exposure to regulatory scrutiny and audit penalties
• Diminished payer credibility with both providers and regulators

While post-payment audits, training webinars, and provider notices offer some mitigation, these traditional approaches are often reactive, untargeted, and labor-intensive. They fail to prevent the root cause: recurring knowledge deficits at the point of care.

The Solution: AI-Driven Knowledge Gap Detection and Micro-Training

AI technology provides a scalable, proactive solution by identifying provider knowledge gaps through two underutilized but powerful data streams:

1. Provider-Submitted Inquiries

Through analysis of the questions providers ask—whether submitted through portals, chatbots, call centers, or help desks—AI can detect patterns that signal uncertainty, misunderstanding, or systemic confusion around billing requirements. These patterns reveal areas where clarification or retraining is most urgently needed.

2. Denied Claims Analysis

AI systems can identify trends among denied claims—based on provider specialty, procedure type, diagnosis codes, or documentation errors—that indicate where providers repeatedly go wrong. These insights enable payors to proactively address gaps in compliance or understanding before additional errors occur.

Together, these data sources inform:

• Customized Micro-Training Modules

Concise, issue-specific training is tailored to the provider’s role, specialty, and individual or group-level behavior. Examples include clarifying time documentation in behavioral health services or correcting modifier usage in telehealth claims.

•  Automated, Integrated Delivery

Micro-trainings can be deployed through existing provider engagement channels—portals, EHR plug-ins, or direct email—minimizing disruption while maximizing accessibility and engagement. Delivery platforms can track engagement and completion for compliance tracking and outcome evaluation.

Benefits to Health Care Payors

Adopting an AI-based micro-training solution delivers widespread improvements across financial, operational, and relational dimensions:

• Reduced Denial Rates: Precision education directly addresses root causes of improper claims.
• Lower Administrative Costs: Less need for manual rework, provider retraining, and appeals handling.
• Accelerated Payment Timelines: Cleaner claims allow faster adjudication and payment.
• Enhanced Provider Experience: Targeted, respectful education improves collaboration without increasing provider burden.
• Audit Preparedness: Documented training efforts tied to risk patterns demonstrate proactive compliance management.

Real-World Example: Targeted AI-Driven Provider Education

To illustrate the impact of this approach, consider a mid-sized health care payor that implements an AI solution focused on behavioral health claims—specifically those with a high rate of denials.

After analyzing both denied claims and provider-submitted questions, AI can identify a recurring problem: providers were inconsistently documenting session time for time-based psychotherapy codes. This issue, though technical in nature, was responsible for a significant portion of denials and payment delays.

Using this insight, the payor can deploy a short micro-training module that clarified documentation requirements and includes examples of compliant notes. The training can be automatically distributed to affected behavioral health providers through the payor’s provider portal and tracked for completion.

Marked improvements you can expect to see:

• A significant reduction in denials related to psychotherapy time documentation
• Improved compliance scores during internal audits
• Positive feedback from providers regarding the relevance and brevity of the training

This case demonstrates how AI can translate data into actionable education, improve documentation quality, and support faster, more accurate claim adjudication.

Conclusion: AI Is the Future of Provider Education

The days of generic, reactive provider education must give way to data-informed, adaptive learning experiences. By using AI to proactively detect and address knowledge gaps, payors can move from reactive cost containment to proactive risk reduction.

This approach does more than save money—it transforms how providers and payors interact, laying the groundwork for trust, transparency, and mutual success. Ultimately, investing in intelligent provider education isn’t just a compliance tactic—it’s a strategic lever to improve payment integrity, operational efficiency, and patient outcomes.

Take the Next Step

To learn more about how Constellation Quality Health’s AI-driven provider education solutions can help your organization reduce improper payments and close the provider knowledge gap, contact us today.

About the Author

Richard Mennuti is the Associate Vice President of Fraud, Waste & Abuse at Constellation Quality Health. As a retired FBI Special Agent, Mr. Mennuti brings more than 25 years of combined experience in federal law enforcement and private-sector health care fraud prevention, with a deep focus on proactive payment integrity solutions.