Genetic Algorithm Optimizing PPO Network Stack Cost Reduction
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Solution Overview
Problem
Current methods for optimizing preferred provider organization (PPO) network stacks for electronic medical bill repricing are computationally expensive and inefficient, requiring significant resources due to the large number of possible combinations and the need for iterative processes like automated grid search.
Innovation Solution
A method utilizing a genetic algorithm to iteratively apply and resample PPO network stacks based on cost reduction values, modifying them through crossover or mutation operations until convergence criteria are met, to generate optimized network stacks with reduced resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated grid search is used to analyze every combination of PPO networks, then optimal network stacks can be generated, but significant computing resources and time are consumed
Solution Approach 1:
The patent applies preliminary action by using genetic algorithms to pre-evaluate and rank PPO network combinations based on historical transaction data before actual optimization is needed. This preliminary evaluation creates a foundation of pre-analyzed network performance that reduces the computational burden during actual optimization, allowing faster convergence to optimal stacks without exhaustive grid search of all possible combinations
Solution Approach 2:
The patent implements feedback mechanisms by continuously evaluating network stack performance against actual transaction outcomes and using this feedback to iteratively improve the genetic algorithm's selection and crossover operations. The system learns from past performance data to refine its predictions and reduce the search space for optimal configurations, thereby reducing computation time while maintaining reliability
2Reliability
If automated grid search analyzes every combination of PPO networks, then optimal network stacks can be generated, but computationally expensive processes are required
Solution Approach 1:
The patent applies segmentation by dividing the vast search space of PPO network combinations into manageable segments or populations that can be evaluated independently through genetic algorithms. Instead of exhaustively analyzing every possible combination, the system segments the problem into multiple parallel evolutionary searches, each working on a subset of potential solutions, thereby reducing overall computational resource requirements while maintaining the ability to discover optimal configurations
Solution Approach 2:
The patent employs the principle of cheap short-living objects by using approximate evaluation metrics and surrogate models that provide quick, low-cost estimates of network stack performance. These inexpensive evaluations allow the system to screen large numbers of candidate stacks without requiring expensive, time-consuming detailed analyses, thereby reducing computing resources while still identifying promising candidates for进一步优化
3Adaptability or versatility
If the number of PPO networks is increased to provide more options, then more combinations are possible for optimization, but the complexity of finding optimal stacks increases
Solution Approach 1:
The patent applies dynamics by implementing adaptive genetic algorithms that adjust their search strategies based on the current state of the population and the complexity of the combinatorial space. The system dynamically modifies crossover rates, mutation probabilities, and selection pressures to efficiently navigate the exponentially growing search space as more PPO networks are added, maintaining optimization effectiveness despite increasing adaptability options
Data Source
AI summary
Methods, non-transitory machine readable media, and network stack analysis devices that generate optimized preferred provider organization (PPO) network stacks are disclosed. With this technology, electronic transactions are applied to each of a first plurality of network stacks to determine a cost reduction value for each of the first network stacks. Each of the first network stacks includes an ordered subset of networks. The first network stacks are resampled based on the determined cost reduction values. A determination is made when one or more convergence criteria are met by the resampled first network stacks. When the determination indicates that the convergence criteria are not met by the resampled first network stacks, one or more of the first network stacks are modified based on genetic crossover or mutation operation(s) to generate a second plurality of network stacks. The application, resampling, and determination are then repeated for the second network stacks.