Healthcare Data Aggregation via Cloud Staging Platform
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Solution Overview
Problem
Computing platforms that process healthcare data face challenges such as increased processing resources and bandwidth consumption due to the need to duplicate data across disparate data centers, and risk data loss from natural or man-made disasters, especially when data centers have physical size constraints and rack availability issues.
Innovation Solution
Implementing a cloud computing platform with a data collector service that uploads healthcare data to a staging platform for quick access and durable replication across multiple servers, allowing for low-latency processing and long-term storage in geographically-disparate data centers, ensuring data availability and disaster recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If healthcare data is replicated across multiple geographically-disparate data centers, then data availability and disaster recovery are improved, but processing resources and bandwidth consumption increase due to duplicate data uploads
Solution Approach 1:
The patent consolidates data storage and management operations by implementing a centralized cloud computing platform that receives and processes healthcare data once, then distributes it to multiple data centers. This merging approach eliminates redundant data uploads and processing operations, reducing overall computational resource consumption while maintaining data availability across distributed locations.
Solution Approach 2:
The patent introduces a cloud computing platform as an intermediary between data sources and multiple data centers. This intermediary receives healthcare data once, processes it centrally, and then distributes it to geographically-disparate data centers, eliminating the need for each data center to independently crawl and process the same data source.
2Speed
If processing nodes are co-located with healthcare data in data centers, then data access speed is improved, but physical size constraints and rack availability limitations worsen
Solution Approach 1:
The patent transitions from a single-location data center model to a multi-dimensional distributed architecture where data is replicated across geographically-disparate data centers. This dimensional expansion allows processing nodes to be physically closer to various user locations while maintaining data accessibility, effectively overcoming physical size constraints of individual data centers.
3Reliability
If data is stored in multiple geographically-disparate data centers, then disaster recovery capabilities are improved, but data center hosting costs increase
Solution Approach 1:
The patent merges data management operations into a centralized cloud platform that handles data reception, processing, and distribution to multiple data centers. This consolidation reduces redundant operations and optimizes resource utilization, thereby reducing overall hosting costs while maintaining disaster recovery capabilities across distributed locations.
Solution Approach 2:
The patent implements efficient data replication strategies where the centralized cloud platform creates and distributes data copies to multiple data centers only when necessary, rather than maintaining continuous synchronous replication. This selective copying approach reduces bandwidth consumption and hosting costs while ensuring data availability for disaster recovery.
Data Source
AI summary
Methods, systems, and computer-readable media are provided for aggregating, partitioning, and storing healthcare data. Healthcare data is collected from various disparate healthcare data sources. The data is aggregated into batches of the same type of data. From here, the data is partitioned according to the data's originating healthcare data source. The aggregated and partitioned healthcare data is then stored in a long term storage data store. This system of storing healthcare data allows for efficient retrieval and processing by computing solutions that need access to batches of healthcare data. The system also reduces costs associated with storing data as duplicate storage is eliminated.