Personalized Provider Ranking Using Cost, Quality, and Member Match
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Solution Overview
Problem
Existing provider search systems fail to personalize rankings based on individual member characteristics and provider interactions, often placing sub-optimal providers at the top due to reliance on aggregated, high-level information without considering granular member-level and provider-level data.
Innovation Solution
A method that analyzes member and provider characteristics using precomputed vectors and latent weights to generate a personalized relevancy rank for providers, incorporating factors like cost, quality, and interaction history, enabling fast and accurate provider recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If providers are ranked based on aggregated provider-level information only, then the search process is simple and fast, but the ranking is not personalized and may embed sub-optimal providers at the top
Solution Approach 1:
The patent segments the provider ranking into multiple independent scoring components: a relevance score based on member-provider match characteristics, a quality score based on provider performance metrics, and a cost score based on provider fees. Each component can be calculated independently using different data sources and methodologies, then combined to produce the final personalized ranking. This segmentation allows the system to achieve high personalization accuracy while maintaining computational efficiency by processing each component separately rather than requiring complex integrated analysis.
2Reliability
If granular member-level and provider-level information is considered, then personalized rankings are achieved, but the computation time and processing complexity increase
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing provider performance metrics, cost data, and member characteristics in databases before the actual search query is processed. When a member searches for providers, the system retrieves pre-processed data and applies weighting factors to generate personalized rankings quickly, rather than performing complex analyses in real-time. This pre-computation approach maintains high recommendation accuracy while significantly reducing search processing time.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the weighting factors of different scoring components (relevance, quality, cost) based on member preferences and historical interaction data. The system can change these weights in response to member behavior patterns, allowing it to optimize the balance between accuracy and processing speed. By adapting the parameters (weights) rather than changing the fundamental computation approach, the system maintains reliability while managing processing time through learned optimizations.
3Adaptability or versatility
If multiple factors including cost, quality, and interaction history are analyzed, then personalized provider recommendations are improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent merges multiple data processing operations into a unified scoring framework where relevance, quality, and cost factors are combined through a single personalized ranking algorithm. Instead of processing these factors as separate complex subsystems, the patent integrates them into one cohesive evaluation process that uses weighted sums of pre-computed scores. This merging approach maintains high personalization capability across multiple factors while reducing overall system complexity by eliminating redundant processing steps and data structures.
Data Source
AI summary
A method for recommending one or more providers to a member may include receiving a provider request including a member identifier and member specified criteria; retrieving one or more provider identifiers for association with the member specified criteria; retrieving one or more member characteristics for association with the member identifier; for each provider identifier, retrieving one or more provider characteristics for association with the provider identifier; analyzing the member characteristics and the provider characteristics to generate an ordered list of the providers; and transmitting the ordered list of the providers to the member computing device via the provider search interface. The ordered list of the providers may be based upon a relevancy rank of each provider that is specific to the member. The relevancy rank may be a function of at least one of a provider cost factor, a provider quality factor, or a weighted combination thereof.