ML-Based Survey Data Extraction Automation
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Solution Overview
Problem
The process of extracting clinical patient information from medical surveys is time-consuming and heavily manual, requiring significant effort to convert paper-based surveys to electronic format.
Innovation Solution
A computer-implemented method that uses machine learning models to extract data from scanned medical surveys by identifying question and answer sections, applying computer vision for image recognition, and accumulating responses, which can then be structured into JSON/XML/CSV data types for upload into a study system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual data extraction from paper surveys is used, then data accuracy can be maintained through human review, but processing time increases significantly (45 min to 8 hours per survey)
Solution Approach 1:
The patent replaces the manual mechanical process of data extraction with an automated computer vision system. The system uses machine learning models to perform image recognition, text extraction, and data validation, substituting human operators with an automated digital workflow that processes surveys in seconds rather than hours.
Solution Approach 2:
The system creates digital copies of paper surveys through scanning or image capture, then processes these copies through automated recognition algorithms. This allows the original physical survey to be replicated in digital form and processed without requiring manual transcription, dramatically reducing processing time while maintaining data integrity.
2Productivity
If automated machine learning models are used for data extraction, then processing speed increases dramatically, but system complexity increases due to multiple models and validation steps
Solution Approach 1:
The patent divides the data extraction process into distinct sequential stages, each handled by a specialized machine learning model: image preprocessing, text recognition, data validation, and result compilation. This segmentation allows each model to focus on a specific task, improving overall efficiency while making the complex system more manageable and maintainable.
Solution Approach 2:
The system performs preliminary validation and confidence scoring during the extraction process itself, rather than requiring post-processing review. By built-in validation checks and confidence threshold filtering during the automated extraction, the system reduces the need for manual verification while maintaining high data quality standards.
3Reliability
If multiple machine learning models with validation are used, then data accuracy and confidence levels improve, but processing time increases
Solution Approach 1:
The patent implements a continuous processing pipeline where multiple machine learning models operate in sequence without interruption. Each model builds upon the previous model's output, with confidence scoring and validation occurring continuously throughout the extraction process rather than requiring separate review stages, thereby maintaining high accuracy while minimizing delays.
Data Source
AI summary
A computer-implemented method of extracting data, such as clinical patient information from surveys such as medical surveys is disclosed herein. The method comprises obtaining completed surveys, each completed survey comprising answers to preconfigured questions, identifying portions of the survey corresponding to answers, applying a machine learning model to the portions identified as answers to predict answers, and accumulating a response to the survey based on the predicted answers.