Generative AI for Enrollment Integrity.
A secure, containerized LLM analyzes and explains enrollment discrepancies with over 90% accuracy to transform operations with explainable, future-adaptive AI.
Challenge
Following a major system launch, a leading Health Plan faced a surge of complex enrollment data discrepancies that required deep root-cause analysis across multiple data sources. Manual resolution was resource-intensive and risked diverting critical enrollment operations staff. The Health Plan needed an automated, explainable AI solution to rapidly detect, diagnose, and resolve data anomalies—without exposing sensitive data or compromising compliance.
Solution
Plan Health engineered a secure, Generative AI architecture that enabled the Health Plan to deploy its own containerized Large Language Model (LLM) within its private environment—an approach we branded Bring Your Own Model (BYOM).
The BYOM leveraged data definitions and knowledge graph for enhanced natural language query understanding, and automated discrepancy summarization to interpret enrollment data and explain variances in plain English. Importantly, no data ever left the Health Plan’s secure network, ensuring full compliance with HIPAA and internal data-governance policies.
Results
Initial BYOM accuracy rates were approximately 50%, typical for untrained models in complex multi-source data environments. Plan Health then introduced an adaptive prompt-initialization framework and dynamic feedback loops that refined model context and diagnostic output for each new data ingestion.
With these enhancements, BYOM accuracy increased to over 90%, delivering near-real-time discrepancy reasoning and freeing operations teams to focus on higher-value work including resolving the discrepancies.
The solution established a scalable, explainable AI framework for continuous data quality improvement and helped the Health Plan kick-start its future-adaptive AI operations.
Let us help you launch your Generative AI for Enrollment Integrity with BYOM.