LLM Data Normalization for Medical Device Quality Analysis
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Solution Overview
Problem
The integration and analysis of diverse quality management data in medical devices and other industries face challenges due to differences in data formats, languages, and scalability, leading to misclassification of customer feedback and potential regulatory noncompliance.
Innovation Solution
A system utilizing large language models (LLMs) to preprocess and normalize data, automatically classify customer feedback, and flag potential safety issues, ensuring compliance with regulatory standards by transforming raw data into a canonical format and providing quality predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from diverse sources (clinical trials, electronic health records, manufacturing records) are integrated, then comprehensive quality analysis is achieved, but data integration complexity and format standardization challenges increase
Solution Approach 1:
The system segments the complex data integration process into distinct modules: data ingestion module that accepts multiple formats, preprocessing module that cleans and standardizes data, normalization module that converts to canonical format, and analysis module that generates insights. This segmentation allows each module to handle specific tasks independently, reducing overall integration complexity while maintaining comprehensive information capture.
Solution Approach 2:
The patent introduces an intermediary normalization layer that acts as a mediator between diverse data sources and the analysis engine. This intermediary component converts various data formats (PDFs, Excel files, database records) into a unified canonical format, enabling seamless integration without requiring changes to source systems or complex custom integration logic for each data type.
2Loss of information
If large volumes of medical device data (70,000 100-page pdfs) are processed, then complete quality documentation is analyzed, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by implementing automated preprocessing steps that occur before main analysis: PDF parsing and text extraction are performed upfront to convert scanned documents into searchable text; data validation and cleaning are executed in advance to identify and correct errors; and initial normalization is completed beforehand to structure data for faster querying. These preliminary actions reduce the computational burden during actual quality analysis.
Solution Approach 2:
The patent replaces manual mechanical processing of physical documents with automated computational systems. Optical character recognition (OCR) technology substitutes for manual transcription of handwritten notes in PDFs; automated parsing algorithms replace manual data extraction from various formats; and machine learning models substitute for human analysts in identifying quality issues. This substitution dramatically reduces processing time from weeks to minutes while maintaining thoroughness.
3Measurement precision
If manual classification of customer feedback is performed, then accurate categorization is achieved, but misclassification errors and regulatory noncompliance risks occur
Solution Approach 1:
The system implements feedback mechanisms where classification results are continuously validated against regulatory criteria and historical data. The normalization module incorporates feedback loops that check classified feedback against compliance rules, and the analysis engine uses feedback from identified patterns to refine classification accuracy over time. This ensures both high precision in categorization and reliable regulatory compliance through automated verification.
4Ease of manufacture
If traditional data analysis methods are used, then existing processes are maintained, but scalability and handling of expanding data volumes become critical concerns
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data types and formats through a single standardized interface. The normalization layer provides multi-functionality by accepting PDFs, Excel files, database records, and other formats while applying the same processing logic. This universal approach maintains process simplicity through standardized workflows while achieving excellent scalability as the system can accommodate growing data volumes without requiring fundamentally different processing methods.
Data Source
AI summary
A system may receive, from a client device, a query requesting quality information of a target device. The system may access a set of data records associated with the target device, pre-process the set of data records for extracting raw data associated with the target device from the set of data records, and convert the pre-processed data to normalized data using a first large language model (LLM). The system may apply a second LLM to the normalized data for generating an output result, which includes the requested quality information of the target device. Applying the second LLM may include: retrieving contextual information related to the target device; generating a prompt to the second LLM, the prompt comprising at least the normalized data, the retrieved contextual information, and the query requesting quality information of the target device; and providing the generated prompt to the second LLM to receive the output result.