Neural Network Confidence Assessment Module for Healthcare Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing confidence assessment modules (CAMs) in clinical coding are static and unable to adapt to changes in coding practices over time, leading to decreased accuracy due to their non-incremental learning capabilities, particularly with the transition from ICD-9 to ICD-10 codes, requiring complete retraining which is challenging without a large dataset.
Innovation Solution
A neural network-based CAM capable of incremental learning, allowing refinement with a limited corpus of training documents, enabling it to evaluate new types of coded encounters and adapt to changes in coding practices without the need for a large initial training dataset, using a binary node-based neural network that adjusts weights based on incremental training corpora.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
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, leading to performance degradation
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 internal parameters based on new training data while maintaining previously learned knowledge, thus resolving the contradiction between adaptability and reliability
Solution Approach 2:
The patent changes the parameters of the confidence assessment module by implementing incremental learning that updates neural network weights and biases based on new training corpora. This allows the system to adapt to ICD-10 coding practices while maintaining accuracy through continuous parameter refinement rather than complete retraining, resolving the contradiction between adaptability and reliability
2Adaptability or versatility
If complete retraining is performed to adapt to new coding systems, then accuracy can be maintained, but a large training dataset is required which is challenging to obtain
Solution Approach 1:
The patent applies partial action by implementing incremental learning that processes only new or updated training corpora rather than requiring complete retraining on entire datasets. This allows the CAM to adapt to new coding systems like ICD-10 using smaller, targeted training subsets, resolving the contradiction between adaptability and data volume requirements
Solution Approach 2:
The patent prepares the confidence assessment module for future coding system changes by implementing a neural network architecture that is pre-configured for incremental learning. This preliminary structural preparation enables the system to efficiently adapt to new coding systems with minimal data, resolving the contradiction between adaptability and training data requirements
3Adaptability or versatility
If a neural network-based CAM with incremental learning is implemented, then continuous adaptation is enabled, but the system complexity increases
Solution Approach 1:
The patent implements self-service by enabling the confidence assessment module to automatically perform incremental learning and adapt to new coding practices without requiring manual intervention or complete retraining. The neural network autonomously updates its parameters based on new training data, resolving the contradiction between continuous adaptation and system complexity through automated self-improvement
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.