ML Grouper Code Assignment for Incomplete EMR Clinical Coding
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Solution Overview
Problem
Conventional healthcare grouper systems fail to assign appropriate DRG codes to electronic medical records (EMRs) that lack or have inaccurate clinical codes, leading to inefficiencies in healthcare cost classification.
Innovation Solution
A system utilizing a machine learning model to determine healthcare grouper codes by analyzing EMRs, generating outputs with confidence scores for grouper and clinical codes, and selecting codes based on predefined thresholds to ensure compliance with grouper guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional grouper systems operate on logic defined in terms of clinical codes, then they can assign grouper codes to EMRs with complete clinical codes, but they fail to assign appropriate codes to EMRs without or with inaccurate clinical codes
Solution Approach 1:
The patent replaces the conventional rule-based mechanical system with a machine learning model that can infer appropriate grouper codes even when clinical codes are incomplete or inaccurate. The ML model learns patterns from historical data and can make probabilistic assignments, substituting rigid logical rules with adaptive intelligent processing.
Solution Approach 2:
The patent introduces an intermediary layer between the EMR and the final grouper code assignment. This intermediary ML model processes the EMR data, fills in missing information, corrects inaccuracies, and then determines the appropriate grouper codes, acting as a mediator that bridges the gap between incomplete data and reliable code assignment.
2Reliability
If manual review processes are used to verify clinical codes and grouper code assignments, then accuracy can be improved, but processing time and human resource requirements increase significantly
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically performs both the assignment of grouper codes and the verification of clinical codes without requiring manual human review. The system serves itself by using the ML model to identify and correct errors, filling missing information, and generating confidence scores, thereby eliminating the need for time-consuming manual verification processes.
3Reliability
If complete and accurate clinical codes are required for proper grouper code assignment, then coding compliance is maintained, but the complexity and time required for code assignment increases
Solution Approach 1:
The patent applies preliminary action by using the machine learning model to pre-process EMR data before formal code assignment. The ML model performs preliminary filling of missing clinical codes, correction of inaccuracies, and validation of data quality, so that by the time grouper codes are assigned, the clinical code data is already in optimal condition, reducing the apparent complexity of the assignment process.
Data Source
AI summary
A system includes a non-transitory storage having stored thereon a machine learning model and instructions, that when executed by a processor, cause the processor to generate, via the machine learning model, an output data including a plurality of outputs for a plurality of grouper decision paths determined based on an electronic medical record and predetermined grouper guidelines. Each output for the respective grouper decision path includes: a plurality of symbols including a grouper code symbol, a plurality of decision symbols, and a plurality of clinical code symbols. The instructions further cause the processor to select one grouper code, select one or more clinical codes corresponding to the selected grouper code, determine confidence scores for the selected grouper code and the selected one or more clinical codes, and provide the selected grouper code and the selected one or more clinical codes to an automatic processing application and/or a user interface.