Dynamic Medical Record Masking for Privacy Compliance
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Solution Overview
Problem
Existing methods for securing medical records in cloud environments do not adequately consider user-specific privacy configurations and fail to inform patients of potential data privacy violations, particularly when medical records are shared with external recipients.
Innovation Solution
A computer-implemented method and system that obtains medical records, determines patient privacy preferences, masks relevant data fields based on those preferences, and generates alerts if the masked data fields are tampered with, ensuring that patient information is secured according to individual privacy settings and standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard masking procedure is applied to DICOM files, then default privacy requirements are met, but user-specific privacy configurations are not considered and some private tags may remain unmasked
Solution Approach 1:
The masking configuration is made dynamic by allowing users to define and modify their own privacy preferences. The system transitions from a static default masking configuration to a dynamic one where the masking behavior adapts based on user-specific settings, ensuring both standard compliance and personalized privacy control
Solution Approach 2:
Different masking strategies are applied to different data fields based on user-specific preferences. Instead of uniform masking, the system applies localized masking decisions to specific tags or data fields according to the user's configured privacy requirements, allowing granular control over what information is protected
2Ease of operation
If patient data is stored in cloud environment for accessibility, then data sharing capability is improved, but security risk of patient information increases
Solution Approach 1:
Privacy masking is applied in advance to patient data before it is stored in or shared from the cloud environment. By pre-masking sensitive information according to user preferences, the system eliminates security risks associated with cloud storage while maintaining the accessibility and sharing benefits of cloud infrastructure
Solution Approach 2:
The masking mechanism acts as an intermediary layer between the patient data and the cloud environment. This intermediary process transforms the data into a protected format that can be safely stored and shared in the cloud without exposing sensitive information, thus mediating between sharing needs and security concerns
3Reliability
If more data fields are masked to enhance privacy, then patient anonymity is improved, but information availability for medical purposes may be reduced
Solution Approach 1:
The masking configuration is made dynamic by allowing users to define and modify their own privacy preferences. The system transitions from a static default masking configuration to a dynamic one where the masking behavior adapts based on user-specific settings, ensuring both standard compliance and personalized privacy control
Solution Approach 2:
Instead of masking all data fields uniformly, the system applies partial masking only to specific fields that the user has configured as sensitive. This selective approach ensures that necessary medical information remains available for healthcare purposes while still protecting the specific private information the user wishes to keep confidential
Data Source
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AI summary
A method (300) and system (100) for securing medical record associated with a patient is disclosed. In one embodiment, the method (300) includes obtaining the medical record associated with the patient from a medical record database (102), wherein the medical record comprises a patient identifier, patient information and medical data. Furthermore, the method (300) includes determining preferences of the patient with respect to privacy of the patient information in the medical record. The method (300) also includes masking one or more data fields in the patient information based on the determined preferences. Additionally, the method (300) includes generating the medical record containing the masked data fields in the patient information.