Reinforcement Learning Intervention Recommendation for Multi-Audit Analysis
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Solution Overview
Problem
Existing predictive data analysis systems face inefficiencies in performing exploration-exploitation traversal of an input space and require multiple models for different audit types, leading to increased computational complexity and resource consumption.
Innovation Solution
Utilizing reinforcement learning machine learning models to optimize intervention routines by maximizing reward measures and minimizing loss measures, enabling efficient exploration-exploitation traversal with linear computational complexity, and employing a single model for multiple audit types through a weighted average loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate models are used for different audit types, then the system can handle diverse audit requirements, but the computational complexity and resource consumption increase
Solution Approach 1:
The patent implements a single machine learning model that can perform multiple audit types through a unified architecture. The model processes different audit types (e.g., medical necessity, coding accuracy, fraud detection) using the same computational framework, eliminating the need for separate models for each audit type while maintaining the ability to handle diverse audit requirements
Solution Approach 2:
The patent combines multiple audit type processing capabilities into a single integrated model. By merging the functionality of multiple separate models into one unified model, the system reduces computational overhead and resource consumption while preserving the ability to perform different audit types through a single computational engine
2Productivity
If traditional predictive analysis methods are used, then the system can perform data analysis, but the exploration-exploitation traversal is inefficient and resource-intensive
Solution Approach 1:
The patent implements reinforcement learning where the model receives feedback in the form of reward signals based on prediction accuracy and resource utilization. This feedback mechanism allows the system to dynamically adjust its exploration-exploitation strategy, improving analysis efficiency by learning from past performance and optimizing future computational decisions
Solution Approach 2:
The patent employs dynamic decision-making through reinforcement learning, where the model adapts its behavior in real-time based on current state and historical performance. This dynamic approach enables efficient exploration-exploitation traversal by continuously optimizing the balance between exploring new patterns and exploiting known effective analysis paths, significantly reducing computation time compared to static traditional methods
3Reliability
If multiple models are deployed for different audit types, then comprehensive audit coverage is achieved, but time and resources are consumed
Solution Approach 1:
The single unified model is designed to provide comprehensive audit coverage across multiple audit types including medical necessity verification, coding accuracy checking, and fraud detection. By integrating these functionalities into one model, the system achieves complete audit coverage without the time penalty of sequentially or parallelly deploying multiple separate models
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing intervention recommendation operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform intervention recommendations by using at least one of reinforcement learning machine learning models and event scoring machine learning models.