Industry: Healthcare

Technology Focus: Data & AI | Large Language Models (LLM)

Overview: At a Glance

With a growing number of claim denials and inaccuracies in healthcare billing, a globally renowned cancer treatment and research institution turned to AI-powered automation to address these challenges. By implementing a solution driven by Large Language Models (LLMs), the client was able to intelligently review clinical documentation and optimize the end-to-end claims process.

The result? A remarkable improvement in billing accuracy and operational transparency:

  • ✅ 35% reduction in incorrect claim submissions
  • ✅ 30% fewer denials from payers and insurance providers
  • ✅ 50% less manual effort needed for appeals and claims correction

About the Client

The client is one of the world’s leading cancer hospitals and research centers, consistently ranked at the top for patient care and medical innovation. Each year, the institution treats over 400,000 patients and supports one of the largest cancer research programs in the United States.

The Challenge

The institution faced tens of billions of dollars in potential annual losses due to high rates of claim denials. These denials were largely driven by:

  • Incomplete or inconsistent clinical documentation
  • Errors in coding and claims preparation
  • Increasingly complex payer compliance requirements

The manual nature of claim reviews and appeals also led to operational inefficiencies, contributing to revenue leakage and administrative burden.

The Solution: AI-Powered Claims Integrity Platform

Novel Nex Solutions implemented a robust AI solution that leveraged the power of LLMs to automate and enhance the claims review process. Key components included:

  1. Automated Review of Clinical Documentation
    • Analyzed medical records, physician notes, and claims for completeness and consistency
    • Flagged anomalies, missing elements, and coding discrepancies
  2. Smart Data Extraction and Coding Suggestions
    • Automatically extracted key information for accurate billing
    • Recommended standardized codes based on clinical context
  3. Payer-Specific Claim Assessment
    • Evaluated each claim in the context of individual payer behavior and coverage criteria
    • Adapted submissions to align with evolving insurance protocols
  4. Continuous Monitoring and Compliance Scanning
    • Identified patterns of under-documentation, over-utilization, and regulatory non-compliance
    • Provided real-time alerts for potential risks and gaps

Results & Business Impact

The AI-driven platform transformed the client’s billing operations by enabling:

  • Improved Revenue Cycle Efficiency
    Claims were processed more accurately and faster, reducing the need for manual reviews.
  • Stronger Compliance
    Proactive detection of documentation issues minimized the risk of non-compliance.
  • Real-Time Risk Visibility
    Dashboards and alerts provided stakeholders with ongoing insights into billing risks and performance.
  • Fairer and More Transparent Operations
    The institution achieved a more balanced system that benefited both providers and payers.

Conclusion

Through strategic AI integration, the client not only improved billing accuracy and compliance but also unlocked new levels of operational efficiency. This case demonstrates how healthcare organizations can harness the power of AI and LLMs to tackle long-standing billing challenges and drive sustainable financial performance.