Machine Learning Model for Record Grouping and Code Prediction
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Solution Overview
Problem
Auditing large volumes of records, such as medical records, is time-consuming and resource-intensive due to the variability in record generation and receipt rates, making timely and accurate identification of records requiring additional codes, like HCC codes, inefficient and inconsistent.
Innovation Solution
A method utilizing a trained machine learning model to group records based on associated data, predicting the likelihood of code addition without substantive content review, allowing for differential audit assignment and processing, thereby enhancing efficiency and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If records are audited by substantive content review, then accuracy of code identification is improved, but time consumption and resource intensity increase significantly
Solution Approach 1:
The patent applies preliminary action by using a machine learning model to pre-analyze record metadata and generate predictions about code likelihood before substantive auditing occurs. This preliminary grouping based on metadata patterns identifies high-probability candidates for code addition, allowing auditors to focus their detailed review efforts only on these pre-identified records rather than reviewing all records substantively, thus reducing overall time consumption while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the raw record metadata and the final auditing decision. This intermediary analyzes metadata features (such as record characteristics, patterns, and associated data) to generate predictions about code likelihood, serving as a bridge that filters and prioritizes records before they reach substantive human review, thereby reducing the volume of records requiring detailed audit while preserving identification accuracy.
2Measurement precision
If records are grouped based on substantive content review, then grouping accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies the extraction principle by removing the substantive content of records from the grouping process and using only metadata features instead. The machine learning model is trained on and operates with metadata characteristics (such as record attributes, patterns, and associated data) rather than analyzing full record contents. This extraction of essential grouping information from metadata alone maintains grouping accuracy while dramatically reducing computational resource requirements compared to analyzing complete record substantively.
Solution Approach 2:
The patent uses copying by creating a simplified representation of records through metadata features that capture the essential characteristics needed for grouping without requiring analysis of the full record content. The machine learning model operates on these metadata copies rather than the original complete records, preserving the ability to accurately predict code likelihood while reducing computational complexity and resource consumption.
3Reliability
If all records are processed uniformly, then consistency in audit quality is maintained, but productivity and efficiency decrease
Solution Approach 1:
The patent applies segmentation by dividing records into distinct groups based on machine learning predictions of code likelihood. Records are segmented into high-probability candidates for code addition and low-probability records. This segmentation allows different processing paths: high-probability records receive focused audit attention while low-probability records can be processed more efficiently or with reduced review, thereby maintaining audit quality consistency for critical records while improving overall productivity through differentiated handling.
Solution Approach 2:
The patent applies local quality by applying different audit intensities and processing approaches to different record groups based on their predicted code likelihood. High-probability records receive more rigorous and consistent audit review to ensure quality, while low-probability records undergo lighter processing. This localized quality approach maintains high audit standards where needed while improving overall efficiency by not applying uniform intensive review to all records.
Data Source
AI summary
A method, apparatus and computer program product group records based upon a prediction of the content of the records. In the context of a method, data associated with respective subjects of the records is received and a threshold of a machine learning model is adjusted to satisfy an accuracy requirement for record categorization. In response to analyzing the data, but not the records, by the machine learning model, the method separates, using the machine learning model, the records into the first and second groups with the first group including records that the associated data indicates are more likely to support the addition of a code and the second group including records that the associated data indicates are less likely to support the addition of a code. The method also includes subsequently processing the records in different manners depending upon whether the records are included in the first or second group.