Cloud-Local Medical Data Synchronization and Version Control
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Solution Overview
Problem
Existing cloud-based Picture Archiving and Communications Systems (PACS) face challenges in maintaining continuous workflow and data synchronization across healthcare facilities, especially during network disconnections and version mismatches, which can lead to downtime and inconsistencies in medical image and data sharing.
Innovation Solution
A cloud-based system that synchronizes medical data between a cloud repository and local repositories, allowing facilities to switch access to local data during disconnections, automatically updates local repositories with cloud data upon reconnection, and checks for version mismatches to ensure consistent synchronization settings and data processing across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based PACS is used to improve accessibility and efficiency, then data sharing between facilities is improved, but system reliability deteriorates during network disconnections
Solution Approach 1:
The system divides the PACS architecture into cloud-based components and local components (local repositories and application proxy servers). Each facility maintains local storage and processing capabilities, allowing continuous operation during network disconnections while maintaining cloud connectivity for synchronization when available.
Solution Approach 2:
The system performs preliminary actions by establishing local repositories and application proxy servers at each facility before disconnections occur. These local components are pre-configured to handle operations independently, ensuring system reliability during network outages while maintaining the ability to share data through the cloud when connected.
2Stability of the object's composition
If automatic updates are implemented across all servers, then data consistency is improved, but workflow continuity deteriorates due to required downtime
Solution Approach 1:
The system implements dynamic update mechanisms where the cloud server can execute updates independently and then synchronize with local servers. Updates are propagated selectively based on version comparisons, allowing the cloud to maintain current functionality while local servers update at their convenience without requiring simultaneous downtime across all facilities.
Solution Approach 2:
The system uses version information feedback mechanisms to determine update requirements. When a local server connects to the cloud, the cloud server compares version information and only transmits updates if differences are detected, avoiding unnecessary interruptions to workflow while maintaining data consistency.
3Measurement precision
If version checking is performed on all connections, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The application proxy server at each local facility performs self-service version checking when connecting to the cloud server. The server automatically determines whether updates are needed by comparing version information, eliminating the need for complex centralized version management while ensuring data accuracy through automated verification.
Data Source
AI summary
A method for updating a system that synchronizes medical data between a cloud repository on a cloud server and a plurality of local repositories on a plurality of local servers of healthcare facilities connected to the cloud server. The method includes, by the cloud server: receiving a request to update a medical synchronization application stored on the cloud server and each of the local servers using an update file; transmitting an instruction to each of the local servers to update the medical synchronization application; and executing the update file to update the medical synchronization application on the cloud server. The cloud server executes the update file regardless of whether any of the local servers have executed the update file.