Medical Controller Location Tracking via Network Message Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The movement of medical device controllers, such as the Impella ®< heart pumps, without accurate location information poses a risk to patient safety as remote monitoring systems lack real-time updates on their physical locations, leading to potential delays in responding to alarms.
Innovation Solution
A machine learning-based system that utilizes multiple electronic data stores and modules to automatically associate medical device controllers with physical locations by analyzing network identifiers and messages, inferring and updating location information through machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual location entry is used for medical device controllers, then location information can be recorded, but the information is not always entered when controllers are moved and sometimes entered incorrectly, leading to loss of location information
Solution Approach 1:
The system enables automatic location tracking by having the controller self-report its location through network identifiers without requiring manual intervention. The location information is automatically updated and maintained by the system itself, eliminating the need for manual entry while ensuring accuracy and completeness of location data.
Solution Approach 2:
The patent replaces the manual mechanical process of location entry with an automated electronic system using machine learning algorithms. The system automatically associates network identifiers with physical locations and tracks controller movements through computational methods, substituting human operation with automated electronic tracking.
2Measurement precision
If automatic location tracking is implemented using machine learning, then location information accuracy is improved, but the device complexity increases due to multiple electronic data stores and machine learning modules
Solution Approach 1:
The system is divided into distinct functional modules: electronic data stores for storing location and network identifier information, machine learning modules for processing and associating data, and communication interfaces for data exchange. This segmentation allows each component to perform its specific function efficiently while maintaining overall system manageability despite the increased complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Methods (500) and systems (300, 400) for automatically ascertaining physical location information about a plurality of medical device controllers (100) and tracking changes in the physical locations of ones of the respective medical device controllers. One or more machine learning modules (426, 430, 434) infer the physical locations from computer network messages received from the medical device controllers, including information about computer network components (306, 308, 310, 312) that are proximate ones of the medical device controllers (100).