Machine Learning Risk Factor Identification for Workers Compensation Claims
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Solution Overview
Problem
Current workers' compensation systems lack effective automation for predicting and managing high-risk claims, particularly migratory claims, which can escalate significantly over time due to medical treatment and pharmaceutical costs, leading to inefficiencies in cost management and intervention.
Innovation Solution
An automated system utilizing statistical and machine learning techniques to identify and mitigate high-risk claims by integrating data from various sources, predicting claim outcomes, and suggesting interventions, including the use of neural networks and logistic regression models to classify claims and generate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human adjusters manually review voluminous medical records to identify high-risk claims, then claim assessment accuracy is improved, but processing time and labor costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated machine learning system that processes medical records and claim data electronically. The system uses neural networks and other ML algorithms to automatically identify high-risk claims, eliminating the need for human adjusters to manually plow through voluminous medical records while maintaining high assessment accuracy.
Solution Approach 2:
The system enables self-service automated claim assessment where the machine learning models independently analyze claim data, identify risk factors, and generate recommendations without requiring human intervention. The automated approach allows the system to serve itself in the assessment process, dramatically reducing processing time while maintaining accuracy through continuous learning from claim outcomes.
2Productivity
If conventional automation approaches are used for claim processing, then processing speed is improved, but the ability to predict migratory claims and identify complex risk patterns deteriorates
Solution Approach 1:
The patent transforms conventional binary automation into multi-parameter predictive analysis. Instead of simple yes/no flagging, the system uses neural networks to process multiple parameters simultaneously including medical history, treatment patterns, claimant demographics, and provider behavior, generating continuous risk scores that enable accurate prediction of migratory claims and complex risk patterns.
Solution Approach 2:
The system combines multiple data types and analysis methodologies into a composite predictive model. By integrating medical records, pharmacy data, claimant information, and provider patterns into a unified machine learning framework, the system achieves both high processing speed and accurate prediction of migratory claims, overcoming the limitations of conventional single-purpose automation tools.
3Device complexity
If simple risk scoring models are used, then system complexity is reduced, but the ability to identify complex claim patterns and predict future costs deteriorates
Solution Approach 1:
The patent segments the complex claim assessment task into multiple specialized modules within the machine learning system. Different neural network models handle different aspects such as medical condition analysis, treatment pattern recognition, claimant risk assessment, and provider behavior evaluation. This segmentation allows the system to manage complexity through modular architecture while achieving high prediction precision through specialized analysis of each risk dimension.
Data Source
AI summary
A system performing machine learning to predict and identify claims that have a high likelihood of migrating across a predetermined risk threshold and to generate intervention strategies to mitigate the likelihood of migration. The processing system includes a computer server, database engine, computer programming instructions, network connectivity, associated claims, payment, medical, pharmacy and other relevant data, a plurality of statistical and machine learning algorithms and a method for electronically displaying and attaching the results to a business process. The system will use all available data to analyze the medical treatment pattern of a claimant and based on automated findings make recommendations as to appropriate interventions to positively impact claims costs.