Anonymized Medical Data Export via Secure Hashing
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Solution Overview
Problem
Existing methods for anonymizing medical diagnosis reports with protected health information are cumbersome and can lead to loss of correlation information between studies and potential re-identification of anonymized data, especially in multi-institution scenarios.
Innovation Solution
The method employs a secure hash algorithm, such as SHA-1, to concatenate selected metadata fields with a separator character, generating a mapped ID that is irreversible, ensuring data anonymization without the ability to re-identify patients, and integrates this process directly into data processing systems like RIS and PACS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anonymization methods are used to remove protected health information, then patient privacy is protected, but correlation information between studies is lost and re-identification becomes possible
Solution Approach 1:
The patent transforms patient identifiers by applying a hash function to convert original identifiers into anonymized identifiers that maintain correlation relationships while preventing re-identification. This parameter transformation allows the same patient to be consistently identified across multiple studies without exposing actual identity information.
Solution Approach 2:
The patent introduces an intermediary hashing mechanism that acts as a bridge between original identifiers and anonymized identifiers. This intermediary process ensures that correlation information is preserved through consistent hashing while the one-way nature of the hash function prevents reversal to original identifiers.
2Loss of information
If detailed patient information is retained for accurate diagnosis and correlation, then diagnostic accuracy is improved, but patient privacy and security are compromised
Solution Approach 1:
The patent segments patient information into two distinct parts: correlation information (preserved through consistent hashing) and identity information (protected through one-way hashing). This segmentation allows diagnostic workflows to maintain data relationships while security requirements protect actual patient identity.
Solution Approach 2:
The patent applies parameter transformation through hashing to convert sensitive identifier fields into protected forms that maintain their functional utility for correlation and tracking while eliminating the ability to directly identify patients from the stored data.
3Reliability
If manual anonymization processes are used, then data security can be controlled, but processing efficiency and productivity are reduced
Solution Approach 1:
The patent replaces manual anonymization processes with automated hash-based identifier transformation. This substitution eliminates time-consuming manual review and editing while maintaining security through cryptographic hashing, significantly improving processing throughput.
Solution Approach 2:
The system performs anonymization automatically through integrated hash functions that operate without human intervention. The automated process maintains security controls while eliminating the productivity bottleneck associated with manual anonymization workflows.
Data Source
AI summary
In an embodiment of the present invention, users with the appropriate permission can launch a function inside a system in order to anonymize and export the currently loaded study or studies, or one or more studies identified by a search criteria. The data from the studies that were identified is then anonymized on the system. In an embodiment of the present invention, the data from selected studies is anonymized on a server, and only then transmitted to another network device. In an alternative embodiment of the present invention, the data from selected studies is anonymized on a server, and only then stored to a hard disk or other media.