Anonymized Patient Record Aggregation for Privacy-Safe Health Data Analysis
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Solution Overview
Problem
Current healthcare systems, particularly in jurisdictions without public healthcare, face challenges in aggregating and analyzing patient records due to fragmented data, lack of centralized data collection, and privacy laws, which hinder research and policy development for population-based healthcare policies.
Innovation Solution
A method and system for anonymizing and aggregating patient records by assigning unique identifiers to health care fields, storing anonymized records, and analyzing them in a health informatics database, while maintaining privacy and ethical considerations, enabling comprehensive data analysis and sharing across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If patient records are aggregated and analyzed to improve research and policy development, then the quality and completeness of health data increases, but patient privacy and confidentiality are compromised
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) from patient records before aggregation and analysis. This separation allows the health data to be aggregated for research purposes while the harmful PII elements are taken out and securely stored separately, preventing privacy breaches while maintaining data completeness for analysis.
Solution Approach 2:
The patent introduces an intermediary anonymization layer between the original patient records and the aggregated dataset. This intermediary process transforms identifiable records into anonymized data that can be safely aggregated and analyzed without directly exposing patient identities, thus mediating between data completeness needs and privacy protection requirements.
2Adaptability or versatility
If a centralized data collection system is implemented to enable comprehensive analysis, then data integration and analysis capability improve, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the centralized data collection system into modular functional components: data ingestion modules, anonymization modules, aggregation modules, and analysis modules. Each module handles a specific aspect of the data pipeline, making the overall complex system manageable through clear separation of concerns and independent deployment of each segment.
Solution Approach 2:
The patent designs universal data interfaces and standardized schemas that allow the system to handle multiple types of health data (electronic health records, claims data, research data) through common pathways. This multi-functionality reduces implementation complexity by providing a single framework that adapts to various data sources rather than requiring separate systems for each data type.
3Loss of information
If diverse medical data formats are integrated into a unified system, then the comprehensiveness of health information increases, but data standardization and processing difficulty increase
Solution Approach 1:
The patent applies local quality transformation by maintaining the original format and characteristics of each data source locally while mapping them to a common standardized schema. Each data type (electronic health records, claims data, research data) retains its local quality features through format-specific parsers, while simultaneously being transformed to fit the unified analysis framework, thus handling information diversity without overwhelming processing complexity.
Data Source
AI summary
The present invention provides a method and system for storing and accessing medical records. The method includes anonymizing patient records, aggregating and analyzing the records, and storing the results of the analysis in a health informatics database. The system includes a database with patient records, a processor for anonymizing the patient records, a second database with anonymized patient records, an interface to access the second database, analyze the records and store the results, an output to display the results and a health informatics database to store the correlated record.