Health Data Curation via Weight Boosting for Imperfect Matches
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems fail to effectively curate vast and disparate health data from various sources, leading to disorganized and difficult-to-understand data sets, which can result in errors, redundancies, and inaccuracies that may impact medical decision-making.
Innovation Solution
A computerized method and system that receives user requests, acquires un-curated data, analyzes it for discrepancies, manipulates the data to correct errors and redundancies, and packages it to meet specific curation requirements, sending curated data back to the user while updating an index for future reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sources is collected to provide comprehensive health information, then the quantity and completeness of data is improved, but the complexity of data organization and processing increases
Solution Approach 1:
The patent segments the data processing system into distinct functional modules: data acquisition module, data cleaning module, data integration module, and data delivery module. Each module handles specific tasks in the data curation workflow, making the overall complex system manageable and maintainable while processing large volumes of health data from multiple sources
2Adaptability or versatility
If data from different systems with varying standards and formats is integrated, then the versatility of data sources is improved, but the difficulty of data standardization and interoperability increases
Solution Approach 1:
The patent implements a universal data curation platform that can handle multiple data types, formats, and standards through a common processing framework. The system uses standardized data models and transformation rules that work across different source systems, enabling versatile data integration without requiring separate processing logic for each data source
3Measurement precision
If raw data is processed and curated to correct errors and remove duplications, then the accuracy of data is improved, but the time required for data processing increases
Solution Approach 1:
The patent applies preliminary data cleaning and validation rules during the data acquisition phase, performing basic error detection and duplication prevention before full data integration. This preliminary action reduces the processing burden in later stages and accelerates the overall curation timeline while maintaining high accuracy standards
4Reliability
If comprehensive data curation is performed to ensure data quality, then the reliability of medical decision-making is improved, but the complexity of data management increases
Solution Approach 1:
The patent implements feedback mechanisms where data quality metrics are continuously monitored and fed back to the processing system. This allows automatic adjustment of cleaning rules and validation thresholds, ensuring high reliability for medical decision-making while reducing manual intervention and simplifying data management through self-regulating quality control
Data Source
AI summary
The boosting of the weights related to imperfect matches of electronic records from disparate sources is discussed. The imperfect matches may be in primary data (such as a code) and/or in supplemental data between two or more records that correspond to the same person (such as a patient). The imperfect matches are analyzed to determine whether they are sufficient to warrant de-duplication of those imperfect matches in a final combined record for the person. The boosting of the weights may be based upon any of numerous factors, such as various distance measures between the supplemental information as a measure of how different the supplemental information is between the respective records.