Reinforcement Learning Models for Multi-Audit Intervention Planning
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Solution Overview
Problem
Existing predictive data analysis systems face inefficiencies in performing exploration-exploitation traversal of an input space, particularly in selecting optimal intervention routines for auditing pharmaceutical entities across multiple audit types, leading to suboptimal resource allocation and increased computational complexity.
Innovation Solution
A reinforcement learning machine learning model is employed to generate optimal intervention routines by maximizing reward measures and minimizing loss measures, ensuring a unique subset of event categories is assigned to each timestep, enabling efficient exploration-exploitation traversal with linear computational complexity, and a single model is trained to predict discrepancies across multiple audit types using a weighted average loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate models are used to predict discrepancies for different audit types, then prediction accuracy for each audit type can be maintained, but device complexity and computational overhead increase significantly
Solution Approach 1:
The patent combines multiple audit type prediction models into a single unified model that processes pharmacy claims data and outputs discrepancy predictions across multiple audit types simultaneously. This consolidation reduces device complexity while maintaining prediction accuracy through a multi-output architecture that handles diverse audit types in one integrated system.
Solution Approach 2:
The unified model is designed with multi-functionality to handle multiple audit types (e.g., medical necessity, coding accuracy, fraud detection) within a single predictive framework. This universal model structure allows it to perform various prediction tasks without requiring separate specialized models for each audit type, thereby reducing overall system complexity.
2Reliability
If comprehensive auditing across multiple audit types is performed, then detection completeness improves, but loss of time and computational resources increases
Solution Approach 1:
The unified model performs preliminary prediction of discrepancies across multiple audit types simultaneously during the initial processing stage. By predicting potential issues in all audit categories at once rather than sequentially, the system achieves comprehensive detection completeness while significantly reducing the total time required compared to multiple separate auditing passes.
Solution Approach 2:
The model maintains continuous useful action by processing all audit type evaluations in a single integrated operation rather than interrupting for separate model executions. This continuous processing approach ensures that detection completeness is maintained across all audit types while minimizing idle time and computational overhead between auditing tasks.
3Ease of manufacture
If traditional sequential auditing methods are used, then resource allocation is simpler, but productivity and auditing efficiency decrease
Solution Approach 1:
The system merges multiple sequential auditing operations into a single parallelized model execution. This unified approach maintains relative simplicity in resource allocation through centralized model management while dramatically improving productivity by processing multiple audit types simultaneously rather than sequentially, thereby eliminating waiting time between auditing stages.
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.