ML Grouper Code Assignment for Incomplete EMR Clinical Coding
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Solution Overview
Problem
Conventional healthcare groupers fail to assign appropriate DRG codes to electronic medical records (EMRs) that lack, have partial, or contain inaccurate clinical codes, leading to inefficiencies in billing and revenue cycle management.
Innovation Solution
A system utilizing a machine learning model trained on historical EMRs to determine grouper codes and supporting clinical codes, generating outputs with confidence scores to automate the assignment process, ensuring compliance with grouper guidelines and enabling direct-to-bill workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional groupers 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 with partial or inaccurate clinical codes
Solution Approach 1:
The patent introduces an intermediary system that bridges the gap between incomplete clinical codes and accurate grouper code assignment. This system uses machine learning models trained on historical data to infer missing or correct clinical codes, thereby mediating between the input EMR data and the required grouper code output, resolving the contradiction between handling incomplete data and maintaining assignment accuracy
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on extensive historical electronic medical record data before actual code assignment. This preliminary training enables the model to learn patterns and relationships, allowing it to accurately assign grouper codes even when input clinical codes are partial or inaccurate, thus preparing the system in advance to handle incomplete data effectively
2Measurement precision
If manual review processes are used to ensure accurate code assignment, then code accuracy improves, but billing speed and productivity decrease
Solution Approach 1:
The system enables self-service by implementing automated machine learning models that independently perform code assignment without requiring manual review for every case. The model serves itself by continuously learning from historical data and making autonomous decisions, thereby maintaining high accuracy while dramatically increasing billing speed and eliminating the bottleneck of manual processing
Solution Approach 2:
The patent transforms the code assignment process from a manual, human-reviewed operation to an automated machine learning operation. This parameter change in the processing method—shifting from human cognitive evaluation to algorithmic pattern recognition—maintains or improves accuracy through consistent application of learned patterns while exponentially increasing processing speed and productivity
3Productivity
If automated systems are implemented to increase billing speed, then productivity improves, but accuracy and compliance with grouper guidelines may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models are continuously trained on historical data and their performance is monitored. The model receives feedback from actual code assignment outcomes and uses this to refine its predictions, ensuring that automated high-speed processing maintains or improves accuracy over time through iterative learning and compliance validation
Data Source
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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.