Incremental Neural Confidence Assessment for Healthcare Coding
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Solution Overview
Problem
Current Confidence Assessment Modules (CAMs) in healthcare coding are static and unable to adapt to changes in coding practices over time, requiring complete retraining with large datasets, which is impractical, especially with the transition from ICD-9 to ICD-10 codes, leading to decreased accuracy and the need for manual retraining.
Innovation Solution
A neural network-based CAM capable of incremental learning, allowing refinement with small subsets of coded encounters, enabling continuous adaptation to new types of coded encounters and maintaining high accuracy without requiring a full retraining dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static confidence assessment module is used, then the initial coding accuracy is maintained, but the system cannot adapt to changes in coding practices over time
Solution Approach 1:
The patent implements a dynamic confidence assessment module using a neural network that can continuously learn and adapt to changing coding practices. The system transitions from a static model to a dynamic one by incorporating incremental learning capabilities, allowing the CAM to update its confidence assessments as new coding standards and practices emerge without requiring complete retraining.
Solution Approach 2:
The system changes the parameters of the confidence assessment model over time through incremental learning. By adjusting the neural network weights and parameters based on new training data, the system adapts to evolving coding practices while maintaining reliable performance. This allows the model to evolve its parameters dynamically rather than remaining fixed.
2Reliability
If complete retraining is performed to adapt to new coding standards, then accuracy is restored, but the process is challenging with limited data availability
Solution Approach 1:
Instead of requiring complete retraining with extensive data, the system applies partial learning updates using limited new training data. The incremental learning approach processes only the necessary portion of new information to adapt to coding changes, rather than requiring a full retraining cycle with large datasets. This enables effective adaptation with minimal data.
Solution Approach 2:
The system performs preliminary adaptation through incremental learning before complete retraining would be necessary. By continuously updating the model with new data in small increments, the system maintains accuracy proactively rather than waiting for performance degradation that would require extensive retraining.
3Adaptability or versatility
If a neural network with incremental learning is implemented, then adaptability to new coding practices is improved, but the system complexity increases
Solution Approach 1:
The neural network-based CAM performs self-updating through incremental learning, automatically adapting to new coding practices without requiring complex external retraining infrastructure. The system serves itself by continuously learning from new data, reducing the need for manual intervention and complex data management processes that would otherwise be required.
Solution Approach 2:
The neural network architecture provides universal adaptability across different coding standards and practices through a single unified model. Rather than requiring separate systems for different coding eras, the incremental learning capability allows one model to serve multiple functions and adapt to various coding standards, reducing overall system complexity.
Data Source
AI summary
A system including a confidence assessment module that implements a neural network to assess the likelihood that codes associated with a patient's encounter with a healthcare organization are accurate. The confidence assessment module may also be incrementally trained.