Machine Learning Co-occurrence Model for Missing Billing Code Detection
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Solution Overview
Problem
Healthcare systems face inefficiencies due to missing hospital billing codes in invoices, leading to incomplete cost recovery and increased healthcare costs, as human detection is unreliable and time-consuming.
Innovation Solution
A machine learning-based system that utilizes a co-occurrence model to detect and recommend missing hospital billing codes in invoices by analyzing a set of reference documents, generating a co-occurrence matrix to determine the likelihood of item pairs being present, and providing an indication of missing items with confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human detection methods are used to identify missing billing codes, then the system can detect incomplete information, but the process is unreliable and time-consuming
Solution Approach 1:
The patent replaces manual human review with an automated machine learning system that uses co-occurrence models to detect missing billing codes. The system processes electronic documents and applies trained algorithms to identify incomplete information automatically, eliminating the need for time-consuming human detection while improving reliability through consistent automated analysis.
2Reliability
If comprehensive billing code verification is performed, then cost recovery completeness improves, but processing complexity increases
Solution Approach 1:
The patent introduces a co-occurrence model as an intermediary between the raw document data and the final verification result. This model learns from training data the typical co-occurrence patterns of billing codes and uses these patterns to identify missing items, simplifying the verification process while maintaining high reliability through statistically-derived relationships rather than complex rule-based systems.
3Measurement precision
If manual review processes are used to ensure billing code completeness, then detection capability is maintained, but productivity decreases
Solution Approach 1:
The patent enables the system to perform self-service by automatically training the machine learning model on provided documents and then using the trained model to detect missing billing codes without human intervention. The system serves itself by learning from data and applying that knowledge to identify incomplete information, maintaining detection capability while dramatically improving processing efficiency through automation.
Data Source
AI summary
In some embodiments, there is provided a system. The system may include at least one data processor and at least one memory storing instructions which, when executed by the at least one data processor, cause the apparatus to at least: determine, for a received document including at least one item, that the received document likely includes at least one missing item, the determination based on at least a machine learning model and the at least one item; and provide an indication of the at least one missing item. Related systems and articles of manufacture are also provided.