Anonymized Medical Data Communication via Segmented Network Architecture
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Solution Overview
Problem
Medical establishments face challenges in integrating AI-generated results directly into their PACS or RIS systems due to legal and security concerns, particularly when patient data is processed in a cloud environment accessible via a public network.
Innovation Solution
A method and apparatus for data communication in a network with both medical (PHI) and non-medical (NoPHI) network areas, where patient data is anonymized and sent to a server in the NoPHI area, allowing for secure processing and status notification without direct PHI exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If patient data is sent to an external cloud service for AI processing, then AI algorithm performance and computing capability are improved, but data security and compliance with legal standards deteriorate due to PHI exposure risks
Solution Approach 1:
The system segments patient data into two distinct parts: PHI data (stored locally in the medical establishment's network) and NoPHI data (sent to the cloud service). This segmentation allows the system to leverage cloud-based AI computing power while maintaining data security compliance by keeping sensitive identifiable information within the controlled medical network environment.
Solution Approach 2:
The system introduces an intermediary conversion process that transforms PHI data into NoPHI data using a standardized format (such as DICOM with anonymization). This intermediary step enables secure communication between the local medical system and external cloud services, allowing AI processing without direct exposure of sensitive patient identifiers.
2Reliability
If manual user login and result verification is implemented at the AI system, then data security is improved by requiring user authentication, but system complexity and operational convenience deteriorate
Solution Approach 1:
The system implements automated authentication where the medical establishment's system automatically transmits authentication credentials with the data transmission. The cloud service automatically verifies these credentials and establishes secure connections without requiring manual user login, result verification, or interactive authentication steps, thereby maintaining security while improving operational convenience.
3Reliability
If patient data is anonymized and sent to a public network cloud service, then data security compliance is improved by preventing direct patient identification, but information completeness and traceability deteriorate
Solution Approach 1:
The system creates a copy of the patient data in anonymized form (NoPHI data) for cloud processing, while the original PHI data remains stored locally. The anonymization process preserves all clinically relevant information while removing direct patient identifiers. This copying approach maintains information completeness for AI analysis while ensuring compliance through anonymization, and the local storage of original data preserves traceability capabilities.
Data Source
AI summary
A method is for data communication in a network including a first network area and a second network area. The method includes provisioning medical patient data; provisioning identification data for identification of a patient; provisioning a code linked to the identification data; sending medical patient data and the code from the first network area to a server in the second network area; and processing the patient data by the server. The method further includes provisioning identification data or input of identification data for identification of a patient by the user; establishing of a code linked to the identification data; automatic sending of the code to the server; establishing the status of the processing patient data linked to the code; creating a corresponding status notification by the server; and sending the status notification to the user.