OVERVIEW

With its ability to process vast amounts of data quickly and accurately, AI is transforming the way revenue cycle managers perform advanced data searches across hundreds of millions of eligibility records on behalf of Healthcare Providers. This is accomplished using scalable architecture, robust technologies, and sophisticated analytics.

CLIENT

A US–based Healthcare Revenue Cycle Management (RCM) Service Provider supporting hospitals and healthcare organizations sought a cost-effective, streamlined, and robust system to boost account reimbursements from insurance companies and discover new billable insurance coverage opportunities.
They partnered with Quantrium to develop a highly scalable Coordination of Benefits (COB) Eligibility Verification Solution that managed and analyzed vast healthcare insurance and patient data sources to find the best matches, save time, cut costs, and uncover additional insurance coverage opportunities.

THE QUANTRIUM LEAP FORWARD

• Quantrium designed and built a high-performance and scalable Eligibility Framework that seamlessly connected healthcare providers with major clearinghouses to verify patient eligibility and insurance coverage quickly at scale using HIPAA-compliant X12 EDI transactions.
• The system intelligently parsed and normalized X12 response transactions across multiple insurance plans, leveraging advanced matching and fallback search strategies based on patient demographics. It suppressed redundant coverage data and highlighted the optimal insurance information to help hospitals maximize reimbursement efficiency.
• Simplifying the eligibility verification process in the system, it saved valuable time even as it helped the hospitals foster trust and transparency with patients, assuring them that their coverage information was accurate and clearly communicated from the outset.
• Identifying additional billable insurance coverage, it also increased hospital reimbursements to improve the overall revenue cycle.
• Powered by AI-driven monitoring and anomaly detection, the framework continuously optimized workflows, detecting irregularities in real time, refining the search performance for consistent reliability and speed.

IMPACT

• The Client saw the Eligibility Search Capacity increase by 8X from 2M/Day up to 16M/Day. Additionally, it improved the Search Efficiency by 25% resulting in a cost reduction of 32%.
• The infrastructure cost recorded a 50% reduction, shifting from Docker-Kubernetes to bare metal servers.
• The framework’s unmatched scalability, reliability, and efficiency enabled the Client to extend its implementation to major clearinghouses, manage large volumes, and drive substantial revenue growth.
• Easy integration with leading clearinghouses allowed the platform to process over 5 million eligibility searches every day, scalable to 16 million+ per day with minimal infrastructure overhead.

TECHNOLOGY STACK

Data Modeling

Data Science

Pattern Matching

Python

RabbitMQ

PostgreSQL

Datadog

ALOps