Self-Training Computer-Assisted Coding System for Billing Accuracy
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Solution Overview
Problem
Computer-assisted coding systems in healthcare are often inaccurate and require significant human interaction and time to improve their billing code accuracy, particularly when processing social habit status information such as smoking, drinking, and drug use statuses from various types of healthcare content.
Innovation Solution
A self-training computer-assisted coding system that processes content to define billing codes, receives user feedback to calculate confidence scores, and uses these scores to train the system, thereby improving its accuracy without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CAC systems are used to process healthcare content for billing codes, then billing code generation is achieved, but accuracy is insufficient and significant human interaction time is required
Solution Approach 1:
The CAC system performs self-training by automatically processing user feedback to generate confidence scores and update its own models without requiring manual intervention. The system serves itself by converting feedback into training data, eliminating the need for continuous human training efforts while improving billing code accuracy over time.
Solution Approach 2:
The system implements a feedback loop where user corrections of billing codes are automatically processed to generate confidence scores. These feedback signals are used to retrain the CAC system, continuously improving its accuracy. The feedback mechanism transforms human interactions from time-consuming manual training into automated system learning.
2Reliability
If traditional CAC systems are used, then billing code processing is achieved, but the system requires considerable human interaction to improve accuracy
Solution Approach 1:
The system automatically processes user feedback and performs self-training by generating confidence scores and updating its models without requiring manual intervention. This self-service capability eliminates the need for continuous human training efforts while improving billing code accuracy over time.
Solution Approach 2:
The system performs preliminary processing of user feedback by automatically generating confidence scores before actual model retraining occurs. This preliminary action prepares the training data in advance, making the subsequent model updates more efficient and reducing the need for extensive human involvement in the training process.
3Measurement precision
If manual training methods are used to improve CAC accuracy, then billing code precision can be enhanced, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system replaces manual mechanical training processes with automated computational processes. User feedback is automatically converted into confidence scores and used to retrain models through computational algorithms, eliminating the need for manual data processing and model updating while significantly improving training efficiency.
Solution Approach 2:
The CAC system performs self-training by automatically processing feedback and updating its own models. This self-service capability eliminates the need for external manual training efforts, allowing the system to continuously improve its billing code accuracy without consuming human time and resources.
Data Source
AI summary
A method, computer program product, and computing system for processing content concerning a plurality of patients using a CAC system to define one or more billing codes concerning a social habit status of one or more patients of the plurality of patients. The one or more billing codes concerning the social habit status of the one or more patients are provided to a user for review. Feedback is received from the user concerning the accuracy of the one or more billing codes. The feedback concerning the one or more billing codes is automatically processed to define one or more confidence scores. The CAC system is trained based, at least in part, upon the one or more confidence scores.