Hybrid Decision Tree for Medical Claim Denial Prediction
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Solution Overview
Problem
Healthcare providers face revenue loss and increased costs due to undetermined medical claim denials, as existing prediction systems struggle with categorical variables and unmanageable decision tree depths when predicting claim denials.
Innovation Solution
An AI engine utilizing a decision tree with multiple levels and termination criteria, including historical claim data and denial probabilities, to predict claim denials based on features like procedure codes, payor identification, and patient information, reducing node complexity and improving prediction efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If categorical variables are converted to numeric values using one hot encoding or numerical conversion, then the variables can be used in traditional binary-split trees, but the tree depth becomes unmanageable due to many distinct values in variables like procedure code and diagnostic code
Solution Approach 1:
The patent changes the parameter representation by introducing a hybrid decision tree that handles categorical variables directly without converting them to numeric values. The tree structure dynamically adapts to categorical data by creating branches based on categorical values rather than forcing numeric encoding, thus avoiding exponential tree depth while maintaining the ability to process categorical variables like procedure codes and diagnostic codes.
2Ease of operation
If traditional binary-split trees are used for classification, then the structure is simple and interpretable, but they struggle to handle variables with many distinct values efficiently
Solution Approach 1:
The patent segments the decision tree into different types of nodes: binary-split nodes for numerical variables and multi-way split nodes for categorical variables. This segmentation allows the model to maintain simplicity and interpretability for numerical features while efficiently handling categorical features with many distinct values through specialized multi-way splits, thereby improving overall prediction efficiency without sacrificing interpretability.
Data Source
AI summary
A method includes receiving a medical claim for payment by a payor; and using an artificial intelligence engine to predict whether the medical claim will be denied by the payor, the artificial intelligence engine comprising a decision tree having at least one level, such that each of the at least one level has a split function associated therewith corresponding to one of a plurality of features associated with the medical claim; wherein a node in the decision tree terminates when at least one of a plurality of termination criteria are satisfied.