Failover Mechanism and Leveling Processing for Medical Data
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Solution Overview
Problem
Current event stream processing systems face service interruptions and significant data loss when ESP devices fail, particularly in mission-critical operations like medical data processing, and traditional processing techniques are inadequate for handling large medical data sets in a timely manner.
Innovation Solution
The system implements a failover mechanism for ESP devices to ensure seamless operation without data loss and introduces a leveling processing system that uses a Cartesian merge to generate candidate episode pairs and transition lists, determining attribute relationships between medical episodes to attribute items like costs efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional event stream processing systems are used, then processing speed is maintained, but service interruptions and data loss occur when ESP devices fail
Solution Approach 1:
The system implements a failover mechanism that prepares backup ESP devices in advance. When the primary ESP device fails, the backup device immediately takes over, preventing service interruptions and data loss. This prior cushioning approach ensures continuity of medical data processing without requiring real-time recovery actions.
2Productivity
If traditional processing techniques are used, then system simplicity is maintained, but processing of large medical data sets is inadequate and timely
Solution Approach 1:
The leveling processing system segments the medical data processing into distinct stages: generating candidate episode pairs from medical episodes, creating transition lists from candidate pairs, and determining attribute relationships. This segmentation allows each stage to be optimized independently, improving overall processing efficiency for large medical data sets while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system performs preliminary actions by pre-generating candidate episode pairs and transition lists before final attribute relationship determination. This preliminary processing organizes and pre-computes relationships between medical episodes, reducing the computational burden during real-time processing and enabling timely analysis of large medical data sets.
Data Source
AI summary
Various embodiments include a system having interfaces, storage devices, memory, and processing circuitry. The system may be coupled with one or more storage devices and may receive episode information for a patient from a storage device via one or more wired or wireless links, the episode information includes a plurality of episodes associated with the patient, each of the plurality of episodes is a specific instance of a medical condition. The system may generate a candidate episode pairs list comprising a plurality of candidate episode pairs. Embodiments may also include the system generating a transition list comprising episode pairs from the plurality of candidate episode pairs in the candidate episode pairs list and determining attribute relationships between the plurality of episodes for the patient based on episode pairs in the transition list, the attribute relationships used to attribute items between the plurality of episodes.