AI Payment Prediction Engine for Healthcare Claims

The patent segments the payment prediction process by creating separate prediction models for different payors and service types. Instead of using a single waterfall model, the system divides predictions into multiple categories (e.g., Medicare, Medicaid, private insurers) and further segments by service type (e.g., inpatient, outpatient, emergency), allowing each segment to be predicted with its own specific patterns and characteristics.

The patent changes the parameters of the prediction system by incorporating multiple variables that were previously ignored. These include payor-specific parameters (payment speed, payment amount ratios), service-type parameters (average payment timing), and interaction parameters between payors and services. This transforms the simple waterfall model into a multi-parameter prediction system that captures the complexity of real-world payment variations.

The patent applies preliminary action by pre-calculating and storing payment patterns for each payor and service combination during a training phase. Historical payment data is analyzed in advance to establish baseline metrics (average payment days, payment ratios, denial rates) that are then reused for future predictions. This eliminates the need to perform complex real-time analysis for each new claim, reducing operational complexity while maintaining high accuracy.

The patent creates simplified copies of complex payment patterns by generating representative models for each payor-service combination. Instead of analyzing every individual historical claim, the system creates aggregated payment profiles that capture the essential characteristics of each payor's behavior toward each service type. These copied patterns are then applied to new claims, reducing computational complexity while preserving predictive accuracy.