Automated Clinical Data Anonymization and Identifier Conversion
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Solution Overview
Problem
Current systems for sharing clinical data among healthcare facilities face challenges in data collection and anonymization, leading to increased labor, human errors, and inefficiencies, which hinder the creation of a secure and accessible database for multiple facilities.
Innovation Solution
A medical information processing system that includes clinical databases, identifier conversion processors, anonymization processors, and anonymized databases within each facility, along with a research data management system that converts internal patient identifiers to external ones and anonymizes data, enabling secure sharing and management of clinical data across facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data collection and anonymization are done manually, then data security can be maintained, but labor and efforts increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes with automated computer-based systems. Specifically, it uses automated data collection interfaces that connect to clinical databases, algorithmic anonymization processors that automatically remove personal identifiers, and electronic data transmission systems that securely transfer anonymized data between facilities without human intervention
Solution Approach 2:
The system enables facilities to automatically perform data collection, anonymization, and sharing without requiring manual intervention. The automated pipelines allow facilities to independently manage their own data contribution to the shared database, with systems that self-regulate data quality, security compliance, and transmission protocols
2Reliability
If manual anonymization is performed, then data security is maintained, but time and expense are lost
Solution Approach 1:
The system performs anonymization processing in advance before data sharing occurs. Automated pipelines continuously pre-process clinical data by removing personal identifiers and encrypting sensitive information, so that when data is needed for sharing, it is already prepared and secured, eliminating last-minute manual processing delays
Solution Approach 2:
Manual anonymization tasks are replaced with automated computer-based anonymization processors that use algorithms to identify and remove personal identifiers, apply encryption, and validate data security compliance automatically, dramatically reducing processing time while maintaining or improving security consistency
3Measurement precision
If manual data collection is used, then data accuracy can be monitored, but human errors occur frequently
Solution Approach 1:
Manual data collection and verification processes are replaced with automated computer-based systems that use standardized electronic interfaces, validation algorithms, and error-detection protocols to collect and verify data accuracy, eliminating human transcription errors and inconsistencies while maintaining monitoring capabilities through automated quality checks
Solution Approach 2:
The system implements automated feedback loops where data quality metrics are continuously monitored, validation rules automatically check for errors and inconsistencies, and alerts are generated when anomalies are detected, enabling real-time correction of data quality issues without human intervention
4Adaptability or versatility
If clinical data is shared among multiple facilities, then research capabilities are enhanced, but information security risks increase
Solution Approach 1:
The patent introduces automated anonymization processors and secure data transmission intermediaries that stand between the clinical databases and the shared research database. These intermediaries automatically remove personal identifiers, apply encryption, and validate data security, allowing facilities to share data for research while maintaining security through automated mediation rather than direct exposure
Solution Approach 2:
Manual security review and data preparation processes are replaced with automated computer-based security protocols that systematically apply encryption, validate anonymization completeness, and control data access through electronic authentication systems, enhancing security consistency and reducing human error while enabling broader research access
Data Source
AI summary
A system that includes a clinical database that stores clinical data in association with an internal identifier of a patient. An identifier conversion processor converts internal identifier associated with clinical data to an external identifier. An anonymization processor anonymizes confidential data in clinical data. An anonymized database stores anonymized data including anonymized clinical data and an external identifier associated with the anonymized clinical data. A research database stores anonymized data provided from the anonymized database for individual research projects. A research data provision processor receives a request from a user, reads out from the research database at least part of anonymized data associated with a research project to which the user belongs, and provides it to the user.