Predictive Model for Nurse Intervention Claim Routing
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Solution Overview
Problem
Determining when to utilize a nurse intervention program for injured workers is challenging due to the complexity of injury types and varying insurance claim rules, with existing systems failing to effectively identify which patients benefit most from these services.
Innovation Solution
A computer system that includes a data storage module, predictive model component, model training component, and routing module to analyze historical and current claim transaction data, incorporating determinate and indeterminate information to determine whether a claim should be referred to a nurse intervention program, enhancing the model's performance and decision-making capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple rules are used to determine nurse intervention program eligibility, then the decision-making process is simplified and faster, but the ability to effectively identify which patients benefit most from services deteriorates
Solution Approach 1:
The patent replaces manual rule-based decision-making with an automated predictive model that uses machine learning algorithms. The model processes complex patterns in claim data to predict which patients would benefit most from nurse intervention programs, eliminating the need for manual application of simple rules while improving identification accuracy.
Solution Approach 2:
The patent transforms the decision-making approach by changing from binary rule-based parameters (yes/no eligibility) to probabilistic predictive parameters. The model outputs a predicted likelihood of benefit, allowing for nuanced decision-making that considers multiple factors simultaneously rather than relying on fixed thresholds.
2Measurement precision
If comprehensive information about injury types and claim rules is considered, then the accuracy of nurse intervention decisions is improved, but the time and complexity of the decision-making process increases
Solution Approach 1:
The patent performs preliminary training of the predictive model using historical claim data and outcomes before actual decision-making occurs. During operation, the trained model can quickly predict eligibility for new claims without requiring real-time analysis of complex rules, as the learning has already been done in advance.
Solution Approach 2:
The patent substitutes manual analysis of comprehensive claim rules with an automated neural network that has learned these rules from training data. The model processes complex patterns in a single forward pass, dramatically reducing decision time while maintaining high accuracy compared to manual review of comprehensive guidelines.
3Reliability
If more claim transaction data and account-specific information are included in the predictive model, then the model performance is enhanced, but the device complexity increases
Solution Approach 1:
The patent implements a universal predictive model framework that can handle multiple types of data (claim transaction data, account-specific information, patient demographics) through a single integrated neural network architecture. This multi-functional model processes diverse data types using the same underlying mechanisms, improving performance without proportionally increasing complexity.
Solution Approach 2:
The patent introduces data preprocessing and feature engineering layers as intermediaries between raw data sources and the neural network. These intermediary components standardize and transform diverse data types into appropriate formats for the model, managing complexity by separating data handling from the core predictive logic.
Data Source
AI summary
According to some embodiments, historical claim data may be stored in a computer storage unit. The historical claim data may be used to train and verify a predictive model, the predictive model being associated with an evaluation of claim transactions to determine whether to refer each claim transactions to a nurse intervention program. Data for current claim transactions may be processed, and the trained and verified predictive model may be applied to the data for the current claim transactions to generate a respective output for each of the current claim transactions. Some of the current claim transactions may be selectively routed to a case management center associated with the nurse intervention program based on the outputs generated by the predictive model.