Automated Health Data Abstraction Using NLP and Rules
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Solution Overview
Problem
The current process of submitting health care data to registries is complex, time-consuming, and highly subjective, requiring manual interpretation and judgment by specialized registrars, leading to variability and potential integrity issues in data consistency and quality.
Innovation Solution
An automated system using natural language processing (NLP) and a rules-based engine to interpret and structure health care data from various sources, providing recommendations for abstraction to data registries with rationale and hyperlinks for review by registrars, thereby standardizing the abstraction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data abstraction by specialized registrars is used, then data can be processed with human judgment, but the process becomes complex, time-consuming, and highly subjective
Solution Approach 1:
The patent replaces the manual mechanical process of data abstraction performed by human registrars with an automated computer system that uses NLP and rules-based processing. The system automatically extracts data from EHRs, applies abstraction rules, and generates registry submissions without human intervention, thereby eliminating the time loss associated with manual processing while maintaining data integrity through consistent algorithmic application.
Solution Approach 2:
The patent changes the fundamental parameters of the data abstraction process from manual to automated operation. By transforming the process into a computer-based system that processes data in bulk rather than individually, the system achieves both speed and consistency, resolving the contradiction between processing time and data integrity.
2Reliability
If manual data abstraction by specialized registrars is used, then data can be interpreted with professional judgment, but the process becomes highly subjective and varies significantly from person to person
Solution Approach 1:
The patent replaces the subjective human judgment process with an objective computer-based system that applies consistent abstraction rules. The rules-based engine ensures that the same data is processed identically across all cases, eliminating the variability and subjectivity inherent in manual processes while maintaining the necessary complexity handling through automated logic.
Solution Approach 2:
The patent segments the complex data abstraction process into discrete, manageable rules and steps that can be systematically applied by the computer system. This segmentation allows the system to handle complex data relationships through modular rule application, achieving consistent results without requiring human judgment.
3Productivity
If multiple staff are employed for data abstraction, then comprehensive data processing can be achieved, but the cost and resource requirements increase significantly
Solution Approach 1:
The patent replaces the need for multiple human staff members with a single automated computer system that performs all data abstraction tasks. The system processes data at scale without requiring additional resources, achieving high productivity while eliminating the quantity of human labor required for the function.
Solution Approach 2:
The patent implements a self-service system where the computer automatically performs data extraction, interpretation, and registry submission without requiring human staff. The system serves itself by processing data independently, eliminating the need for ongoing human resource investment while maintaining comprehensive processing capability.
Data Source
AI summary
The present invention relates to a method and apparatus for monitoring a stream of health care data, applying a rules-based methodology to process the data to determine how the data should be structured for abstraction to a data registry, and providing recommendations and rationales in support of the abstraction.