Centralized Pricing Engine for Unpriced Claim Model Selection
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Solution Overview
Problem
Existing computational systems are unsuitable for automatically identifying and implementing optimal pricing models for services or items without a standardized reference, often requiring significant administrator intervention due to differences in data entry and formatting, especially in the context of Certificate of Coverage agreements and Situs State applicability.
Innovation Solution
A centralized pricing engine that routes claim data to multiple pricing models, including internal and external systems, consolidates pricing data, and adjudicates the most appropriate model based on claim data and adjustments, enabling automated identification and implementation of optimal pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple pricing models are executed to identify the optimal pricing model, then pricing accuracy and compliance are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the pricing determination process into multiple independent pricing models (e.g., UCR model, SSP model, OCM model, vendor-based model) that can be executed separately. Each model processes claim data independently and generates pricing results that are then consolidated, allowing for accurate pricing while maintaining manageable model complexity through modular design.
Solution Approach 2:
A centralized pricing engine acts as an intermediary between claim data and multiple pricing models. The pricing engine receives claim data, routes it to appropriate pricing models, consolidates results, and applies adjustments. This intermediary manages the complexity of coordinating multiple pricing models while ensuring accurate and compliant pricing determination.
2Adaptability or versatility
If claim data is routed to multiple external pricing systems, then pricing model versatility is improved, but data transmission and coordination overhead increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring pricing schedules that define priority orders for executing different pricing models. This pre-establishment of execution sequences allows the system to efficiently route claim data through multiple pricing systems without ad-hoc coordination overhead, reducing data processing time while maintaining versatility.
Solution Approach 2:
The centralized pricing engine provides universal functionality by handling multiple pricing models, data formats, and external systems through a single platform. It can route claim data to various pricing models (UCR, SSP, OCM, vendor-based) and consolidate results, eliminating the need for separate systems for each pricing model and reducing overall coordination overhead.
3Measurement precision
If pricing data from multiple models is consolidated and compared, then optimal pricing identification is improved, but computational resource requirements increase
Solution Approach 1:
The system dynamically adjusts the pricing determination process by applying adjustments and modifiers to pricing data based on claim-specific factors. The pricing engine can selectively apply adjustments from an accumulator table and modify pricing results dynamically, allowing for accurate optimal pricing identification while optimizing computational resource usage through selective processing.
Solution Approach 2:
The system implements partial action by executing pricing models in a priority order defined in pricing schedules. Not all pricing models are executed for every claim - the system routes data through models based on their priority and applicability to the specific claim, reducing unnecessary computational resource consumption while maintaining accurate pricing model selection.
4Productivity
If automated routing and adjudication processes are implemented, then administrative efficiency is improved, but system configuration and maintenance complexity increase
Solution Approach 1:
The system implements self-service through automated routing and adjudication processes that determine optimal pricing models without requiring significant administrator intervention. The pricing engine automatically routes claim data to appropriate pricing models, consolidates results, applies adjustments, and generates final pricing determinations, significantly improving administrative efficiency while the standardized automation reduces ongoing configuration complexity.
Solution Approach 2:
The system incorporates feedback mechanisms through pricing schedules that define priority orders and through the consolidation and comparison of pricing data from multiple models. The adjudication process uses feedback from multiple pricing models to determine the optimal pricing, and the system learns from pricing outcomes to improve future routing decisions, enhancing efficiency while managing configuration complexity through structured feedback loops.
Data Source
AI summary
To automate a pricing strategy for an otherwise unpriced service or item, prices may be generated through a plurality of different pricing models, via a pricing engine passing input data to a plurality of discrete pricing models. Those pricing models may pass data back to the pricing engine, which then adjudicates the results of the pricing models to identify a most-relevant pricing model for the particular unpriced service or item.