Laboratory Sample ID Hashing for Privacy and Collision Prevention
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical laboratory data management systems face challenges in ensuring patient data privacy and reducing the risk of sample ID collision during data transmission and processing, particularly when healthcare data packets are transmitted outside secure areas, where IDs can be reused and potentially lead to erroneous analysis.
Innovation Solution
A healthcare computer system employs a string generation module to create anonymized sample identifiers using a one-way function, optionally with a random string, to generate anonymized healthcare data packets, ensuring privacy and minimizing the risk of ID collision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sample identifiers are reused regularly to manage data efficiently, then productivity is improved, but the risk of ID collision increases leading to erroneous analysis
Solution Approach 1:
The patent introduces a cryptographic hash function as an intermediary between the original sample ID and the anonymized ID. This hash function transforms the original ID into a unique anonymized identifier that prevents collisions while allowing efficient data management. The hash function acts as a mediator that preserves the uniqueness property of IDs without exposing the original identifiers, thus maintaining both productivity and reliability.
Solution Approach 2:
The patent transforms the sample ID parameter through a cryptographic hash function, changing its form from the original identifier to an anonymized hash value. This parameter transformation maintains the uniqueness property (preventing collisions) while enabling efficient data management and anonymization simultaneously.
2Adaptability or versatility
If sample identifiers are transmitted outside secure areas for data processing, then adaptability is improved, but patient data privacy is compromised
Solution Approach 1:
The patent extracts the identifying information from the original sample ID by applying a cryptographic hash function. The anonymized ID retains the necessary functional properties for data management but removes the personally identifiable information, thus enabling flexible data transmission while protecting patient privacy.
Solution Approach 2:
The patent changes the parameter form of the sample ID through cryptographic hashing, transforming it from a potentially identifiable identifier to an anonymized hash value. This parameter transformation enables the data to be transmitted outside secure areas for processing while maintaining patient privacy protection.
3Object-affected harmful factors
If anonymization is applied to protect patient privacy, then patient data privacy is improved, but the risk of ID collision increases
Solution Approach 1:
The cryptographic hash function serves as an intermediary that transforms the original sample ID into an anonymized identifier. This intermediary maintains the uniqueness property of the original ID while removing identifiable information, thus preventing ID collisions even though the data is anonymized.
Solution Approach 2:
The patent applies a cryptographic hash function to change the parameter form of the sample ID, transforming it into an anonymized hash value. This parameter change ensures that the anonymized ID maintains uniqueness (preventing collisions) while protecting patient privacy by removing identifiable information.
Data Source
AI summary
A healthcare computer system, the computer system comprising a communications module, a string generation module, a one-way function module and an anonymised data generation module. The communications module is configured to receive one or more healthcare data packets, each healthcare data packet including: data pertaining to one or more medical analytical tests performed on a sample; a sample identifier, identifying the sample; and a timestamp, indicating when the analytical test(s) was performed. The string generation module is configured to generate a string based on the sample identifier and the timestamp. The one-way function module is configured to apply a one-way function to the generated string to generate an anonymised sample identifier. The anonymised data generation module is configured to generate an anonymised healthcare data packet including the data pertaining to the one or more medical analytical tests and the anonymised sample identifier.