Surgical Hub Data Aggregation for Real-Time Perioperative Feedback
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Solution Overview
Problem
Existing surgical systems face challenges in managing the integration and communication of various surgical devices and systems within a sterile field, leading to inefficiencies such as entangled power, data, and fluid lines, and the need for frequent module resets during procedures.
Innovation Solution
A modular surgical hub enclosure that integrates energy generators, smoke evacuation, and suction/irrigation modules, allowing for quick removal and replacement of modules, and facilitates interactive communication between them, along with a centralized communication hub for data management and cloud-based analytics to enhance surgical efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple modular devices are used during surgical procedures, then the quantity and variety of perioperative data increases, but the complexity of data aggregation and management increases
Solution Approach 1:
The surgical hub consolidates data from multiple modular surgical devices into a single centralized platform. The hub receives perioperative data from various devices, aggregates it by event types, and provides unified feedback, eliminating the need for separate data management systems for each device.
Solution Approach 2:
The surgical hub serves multiple functions: receiving data from different device types, determining event types, aggregating data by event type, comparing with baseline data, and providing feedback. This multi-functional approach replaces multiple specialized systems with a single universal platform.
2Measurement precision
If real-time data aggregation and comparison is performed during surgical procedures, then the ability to detect deviations from baseline improves, but the processing time and computational requirements increase
Solution Approach 1:
The surgical hub pre-establishes baseline perioperative data for comparison against real-time aggregated data. By having baseline values ready before procedures begin, the system can quickly compare incoming data without performing complex calculations during critical surgical moments.
Solution Approach 2:
The system continuously compares aggregated perioperative data against baseline data and provides real-time feedback when deviations are detected. This closed-loop feedback mechanism enables rapid detection and response to anomalies without requiring extensive post-procedure analysis.
3Loss of information
If comprehensive perioperative data from multiple sources is collected and stored, then the completeness of patient information improves, but the storage requirements and data management complexity increase
Solution Approach 1:
The surgical hub segments perioperative data by event types, organizing comprehensive data into structured categories. This segmentation allows the system to maintain complete patient information while enabling efficient retrieval and analysis of specific data types without managing all data as a single large dataset.
Data Source
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AI summary
Surgical hubs are disclosed. A surgical hub is configured to communicably couple to a plurality of modular devices. The surgical hub comprises a processor and a memory coupled to the processor. The memory stores instructions that, when executed by the processor, cause the surgical hub to: receive perioperative data from a modular device; determine an event type associated with the perioperative data received from the modular device; aggregate, for each event type, the perioperative data from a plurality of modular devices; compare, for each event type, the aggregated perioperative data from the plurality of modular devices with baseline perioperative data; and provide feedback according to whether the aggregated data for a subject event type deviates from the baseline perioperative data for the subject event type. The perioperative data comprises data detected by the plurality of modular devices during the course of a surgical procedure.