Neural Network Medical Claims Adjudication System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical claims processing systems are prone to fraudulent and erroneous claims, leading to significant annual losses due to manual processes and varying degrees of human error, resulting in unrecouped improper payments exceeding $24 billion annually.
Innovation Solution
A rule-based method utilizing neural computational logic and statistically motivated algorithms for nonlinear dimensionality reduction, combined with artificial intelligence for Medical Code-based decision-making, automates the review and reclassification of medical claims, reducing human intervention and improving accuracy through automated decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes and human review are used for medical claims processing, then flexibility and adaptability are maintained, but error rate increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computer-based system that uses neural networks and machine learning algorithms to process medical claims. This substitution eliminates human error while maintaining high processing speeds, directly resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system enables self-service through automated adjudication where the computer-based system independently processes claims without requiring human intervention for routine decisions. The neural network continuously learns from data and makes autonomous decisions, improving both accuracy and processing efficiency simultaneously.
2Productivity
If automated accounting and documentation systems are used, then productivity increases, but measurement precision decreases due to system limitations
Solution Approach 1:
The patent transforms the processing system from rule-based automated systems to neural network-based systems that can handle non-linear relationships and high-dimensional data. This parameter change in the underlying technology enables both high productivity and high measurement precision by capturing complex patterns in medical claims data that traditional automated systems miss.
Solution Approach 2:
The system combines multiple technological components - neural networks, dimensionality reduction algorithms, and clustering methods - into a composite processing system. This composite approach leverages the strengths of each component to achieve both rapid processing and high accuracy, overcoming the limitations of single-technology automated systems.
3Measurement precision
If human review by clinicians and legal experts is used, then measurement precision improves, but loss of time increases and productivity decreases
Solution Approach 1:
The neural network system performs preliminary analysis of claims data, pre-processing and identifying patterns before final adjudication. This preliminary action by the automated system prepares the data in a way that maintains measurement precision while dramatically reducing the time required compared to starting human review from scratch.
Solution Approach 2:
The patent introduces an intermediary layer - the neural network system - that sits between the raw claims data and the final decision. This intermediary processes and structures the data, maintaining the precision of expert review while operating at automated speeds, thus resolving the time-accuracy tradeoff.
4Productivity
If traditional automated systems are used, then productivity increases, but reliability decreases due to human-dependent workflows
Solution Approach 1:
The neural network system provides a universal processing framework that handles diverse claim types and scenarios consistently. Unlike human-dependent systems that vary by reviewer, this multi-functional system applies the same learned patterns across all claims, ensuring reliability and consistency while maintaining high productivity through automation.
Data Source
AI summary
A context sensitive methodology, a Structured Virtual Construct (SVC) system, data tagging techniques, and an apparatus are provided for performing Medical Code-based decision-making involving the matching of a given medical identified element against one or more of a set of known or reference medical identified elements from history or other data elements. A satisfactory decision is achieved as a function of both aggregated ranking (AR) and account adjudication (AA), where account adjudication refers to the full set of values garnered by the Medical Code accumulation process in the process of generating approval/denial/re-classification/of medical diagnosis and/or claim events.