Surgical Hub Data Transfer Rate Optimization
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Solution Overview
Problem
Current surgical systems lack an efficient method to manage and transfer data from surgical instruments to cloud-based medical analytics networks, particularly in terms of determining the optimal data transfer rate based on available storage capacity, which can lead to data management challenges during surgical procedures.
Innovation Solution
A surgical hub system comprising a storage device, processor, and memory that receives data from surgical instruments and determines the data transfer rate to a remote cloud-based medical analytics network based on available storage capacity, enabling efficient data management and transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is continuously transferred from surgical instruments to cloud-based analytics networks, then data analytics capability is improved, but storage capacity is depleted and data transfer interruptions occur
Solution Approach 1:
The surgical hub continuously monitors the storage device's available capacity and uses this feedback to dynamically adjust the data transfer rate to the cloud. When storage capacity is sufficient, data transfer proceeds at a higher rate; when capacity is low, the transfer rate is reduced or paused, preventing data loss while optimizing cloud analytics capability.
Solution Approach 2:
The system transitions from a static data transfer approach to a dynamic one where the transfer rate is continuously adjusted based on real-time storage conditions. This allows the system to adapt its data transmission behavior to current storage availability, ensuring continuous analytics capability without depleting storage resources.
2Productivity
If data transfer rate is increased to cloud-based analytics networks, then analytics processing speed is improved, but storage capacity is depleted faster
Solution Approach 1:
The surgical hub uses feedback from storage capacity monitoring to regulate the data transfer rate to the cloud. This feedback mechanism ensures that high transfer rates (which improve analytics speed) are only maintained when sufficient storage capacity is available, automatically reducing the rate when storage becomes constrained.
Solution Approach 2:
The system dynamically changes the data transfer rate parameter based on storage conditions. By adjusting this key parameter, the system optimizes analytics processing speed when storage is abundant while preventing storage depletion when capacity is limited.
3Duration of action of stationary object
If data is stored locally with high capacity to maintain continuous operation, then operational continuity is improved, but device complexity increases
Solution Approach 1:
The surgical hub implements an automated feedback-based storage management system that monitors capacity and controls data transfer rates. This eliminates the need for manual intervention or complex manual storage management, maintaining operational continuity through automated decision-making about when and how to transfer data to the cloud.
Solution Approach 2:
The system performs self-management of its storage resources by automatically monitoring capacity and adjusting data transfer behavior without external intervention. This self-service approach maintains operational continuity while avoiding the complexity of manual storage management protocols.
Data Source
AI summary
Various surgical hubs are disclosed. A surgical hub comprises a storage device; a processor coupled to the storage device; and a memory coupled to the processor. The memory stores instructions executable by the processor to: receive data from a surgical instrument coupled to the surgical hub; and determine a rate at which to transfer the data from the surgical hub to a remote cloud-based medical analytics network based on available storage capacity of the storage device.