Cloud-to-Local Medical Image Synchronization System
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Solution Overview
Problem
Current cloud-based Picture Archiving and Communications Systems (PACS) face challenges in ensuring continuous access to medical images and data, particularly when network connections are disrupted, leading to potential downtime and delays in diagnosis due to the need for remote data retrieval.
Innovation Solution
A cloud-to-local, local-to-cloud switching and synchronization system that allows healthcare facilities to automatically retrieve and update medical images and data locally, ensuring continuous workflow even without a network connection, by pre-fetching images and data prior to a patient's scheduled diagnosis and synchronizing them across in-network facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If medical images and data are stored only in cloud-based PACS, then centralized storage and remote accessibility are improved, but access continuity and diagnosis speed deteriorate when network connections are disrupted
Solution Approach 1:
The system performs preliminary actions by automatically pre-fetching and caching medical images and data from cloud-based PACS to local storage at healthcare facilities before they are needed for diagnosis. This advance preparation ensures that when network connections are disrupted, the cached data is already available locally, maintaining access continuity without requiring real-time cloud connectivity
Solution Approach 2:
The system introduces a local cache server as an intermediary between the cloud-based PACS and diagnostic workstations. This intermediary stores copies of medical images and data locally, allowing workstations to access data through the local cache when the cloud connection is unavailable, thus bridging the gap between centralized cloud storage and reliable local access
2Productivity
If medical images and data are pre-fetched to local storage, then access speed and diagnosis time are improved, but data synchronization complexity and storage requirements increase
Solution Approach 1:
The system implements feedback mechanisms where the cloud-based PACS and local cache servers continuously exchange synchronization status information. When data is updated in the cloud, the system receives feedback about the change and automatically updates the appropriate local caches. This feedback-driven approach simplifies synchronization complexity by using event-driven updates rather than complex manual coordination
Solution Approach 2:
The local cache servers perform self-service by automatically determining what data needs to be cached, fetching it from the cloud, and managing local storage without manual intervention. The system autonomously handles synchronization tasks, reducing operational complexity and allowing diagnostic personnel to focus on their primary functions without managing data synchronization
3Adaptability or versatility
If cloud-based PACS is used for centralized storage, then data sharing between facilities is improved, but network dependency and potential downtime increase
Solution Approach 1:
The system performs preliminary actions by pre-fetching and caching commonly accessed medical images and data from cloud-based PACS to local storage at each healthcare facility before they are needed. This advance preparation ensures that when network connections are disrupted, the cached data is already available locally, eliminating downtime and maintaining uninterrupted access to diagnostic information
Solution Approach 2:
The system introduces local cache servers as intermediaries between the centralized cloud-based PACS and local diagnostic workstations. These intermediaries maintain local copies of medical data, enabling facilities to share and access data through the local cache when cloud connectivity is unavailable, thus preserving data sharing capability without network dependency
Data Source
AI summary
A method is provided for advanced-retrieval of medical data in 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 is executed by the cloud server and includes: receiving diagnostic reservation information, that includes timing information for a future diagnosis date and diagnosis information that associates the diagnostic reservation information with a medical image stored in the cloud server; receiving an advanced-retrieval request that comprises an advanced-retrieval time period; determining, in response to receiving the advanced-retrieval request, that the timing information of the diagnostic reservation information overlaps with the advanced-acquisition time period; transmitting in response to the medical image being associated with the diagnostic reservation information, the medical images and data.