AI-Powered Claims & Billing Automation
Healthcare Network
Overview
The client, a multi-state healthcare provider network, was struggling with high claims rejection rates (over 18%) and an average billing cycle latency...
Business Challenge
The client, a multi-state healthcare provider network, was struggling with high claims rejection rates (over 18%) and an average billing cycle latency of 45 days. Manual verification of electronic health records (EHR) against insurance billing codes created severe administrative bottlenecks and delayed revenue capture.
HyperCode Solution
HyperCode designed and deployed an automated agentic AI document parsing pipeline. Leveraging Azure OpenAI models, Python workflow orchestration, and LangChain, the solution automatically extracts diagnostic details from EHR clinical notes, cross-checks them against ICD-10 medical coding databases, and highlights compliance anomalies before claim submission.
Implementation & Consulting Strategy
We initiated the project with a 3-week discovery phase mapping the client's electronic health record database structures. We built high-throughput ingestion adapters using Python and Docker to process incoming clinical records. The heart of the platform utilizes custom LLM extraction chains that identify diagnosis and procedure matches. We implemented a human-in-the-loop review interface for anomalies scoring below a 95% confidence threshold, ensuring clinical accuracy while maximizing automation.
Business Results & Outcomes
The automated claim validation system led to a 50% improvement in customer billing response times, lowered claims rejection rates to less than 3%, and reduced billing error rates by 85%. Annual administrative savings reached $1.2M.
Project Information
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