ML-Based Claim Prioritization and Dynamic UI Updates
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Solution Overview
Problem
Existing methodologies for recovering overpaid claims do not prioritize them based on the likelihood of recovery, leading to suboptimal recovery results, as they rely on human-driven approaches that cannot efficiently analyze time-independent and time-dependent factors.
Innovation Solution
Implementing machine learning models to predict the likelihood of recovery for each claim and dynamically update the user interface to prioritize claims based on predicted scores, considering both time-independent and time-dependent features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-driven approaches are used to prioritize overpaid claims, then the system is simple to operate, but the recovery effectiveness is suboptimal because human analysts cannot efficiently analyze time-independent and time-dependent factors
Solution Approach 1:
The patent introduces machine learning models as an intermediary between the claim data and the prioritization decision. The ML models analyze both time-independent factors (claim characteristics, provider history) and time-dependent factors (temporal patterns, changing conditions) to generate priority scores, thereby improving recovery effectiveness without requiring human analysts to directly process these complex factors
Solution Approach 2:
The patent replaces the mechanical human-driven prioritization process with an automated machine learning-based system. The ML models automatically process claims data, evaluate multiple factors simultaneously, and generate prioritized queues, substituting human cognitive processes with computational algorithms that can efficiently analyze both static and temporal features
2Measurement precision
If machine learning models are implemented to predict recovery likelihood, then the prioritization accuracy is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the prioritization process into distinct components: time-independent feature extraction, time-dependent feature extraction, ML model prediction, and queue generation. This segmentation allows each component to be optimized independently and facilitates parallel processing, reducing overall computational complexity while maintaining prioritization accuracy
Solution Approach 2:
The patent performs preliminary actions by pre-processing claims data to extract both time-independent and time-dependent features before ML model execution. This pre-computation of features reduces the computational burden during actual prioritization operations and enables faster real-time processing of new claims
3Productivity
If claims are sorted by dollar amount in existing methodologies, then the interface is simple to implement, but the recovery results are suboptimal because it does not consider likelihood of recovery
Solution Approach 1:
The patent implements a dynamic prioritization system where claim priorities are not fixed but continuously updated based on ML model predictions. The system generates time-varying priority scores that reflect changing recovery likelihoods, allowing the interface to adapt to new information and improving overall recovery productivity
Solution Approach 2:
The patent changes the sorting parameter from simple dollar amount to a composite priority score that incorporates both financial value and predicted recovery likelihood. This parameter transformation enables the system to prioritize claims based on expected recovery value rather than just nominal amount, significantly improving recovery results
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same.