Machine Learning Payor Class Prediction for Medical Claims
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Solution Overview
Problem
The insurance industry faces inefficiencies and inaccuracies in analyzing medical claims for payment, as current methods rely heavily on human analysis, which is slow and prone to errors, and there is a need for a more effective way to determine the correct payor class for medical claims across different insurance types.
Innovation Solution
A machine-learning system that analyzes medical claim data to predict the appropriate payor class by identifying medical codes, standardizing them, training models using historical data, and applying these models to new claims to generate predictions and determinations, thereby automating the process of identifying the correct payor for medical claims.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysis is used to determine payor class for medical claims, then accuracy can be maintained through expert judgment, but the process becomes slow and inefficient
Solution Approach 1:
The patent replaces the mechanical system of human expert analysis with an electronic machine learning system that processes medical claims data. The system uses trained algorithms to automatically determine payor class coverage, substituting human cognitive processes with computational processes that can handle large volumes of claims rapidly while maintaining consistent application of coverage rules.
Solution Approach 2:
The machine learning system is trained on historical claims data and payor class determinations, enabling it to autonomously make payor class predictions without requiring human intervention for each individual claim. The system self-corrects and improves through continuous learning from the training data, making independent determinations that can be reviewed if necessary.
2Productivity
If machine learning algorithms are used to analyze medical claims, then processing speed and efficiency improve, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex machine learning system into distinct functional modules: a training module that processes historical claims data and payor class determinations to build predictive models, and an analysis module that applies these trained models to new claims. This segmentation allows each module to be optimized independently and simplifies the overall system architecture by separating the complex training process from the relatively simpler prediction process.
Solution Approach 2:
The system performs preliminary action by training the machine learning models in advance using historical claims data and known payor class determinations. This pre-training phase creates ready-to-use predictive models that can be rapidly applied to new claims without requiring complex real-time computation, thereby simplifying the operational phase while maintaining high processing speed.
3Measurement precision
If manual review of each medical claim is performed, then accurate payor determination can be achieved, but the time and resources required increase significantly
Solution Approach 1:
The patent creates a computational copy of the human expert decision-making process through machine learning models trained on historical claims and payor class determinations. Instead of requiring actual human experts to review each claim, the system uses these trained models to replicate and apply coverage determination logic at scale, dramatically reducing time per claim while maintaining accuracy through the fidelity of the trained models.
Data Source
AI summary
The disclosed invention is directed to methods and systems for machine-learning analysis of medical-claim pay class coverage. The machine-learning analysis may predict whether a third-party payor class is the proper payor class for a medical claim sent to a health insurance company or directly to the patient. The system may communicate payor class determinations between devices in a computer network. A payor class determination may be based on a computed likeliness score from a trained machine-learning model. The analysis may include identifying medical codes from historical medical claims, standardizing the medical codes, screening the historical medical claims based on the medical codes, training a model based on the standardized medical codes and corresponding payor class determinations, and applying the model to new medical claims to generate predictions and determinations.