Virtual Model Training for Remote Medical Device Safety
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex medical devices in home settings pose challenges due to their complexity, requiring proper operation and maintenance to ensure patient safety, which can be difficult without adequate training and immediate issue resolution.
Innovation Solution
A system that builds a virtual model of a medical device for training patients and analyzing care data from sensors using a cloud service, employing machine learning to score data and trigger emergency procedures when thresholds are exceeded, allowing for remote monitoring and real-time guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If complex medical devices are deployed in individual homes, then patient convenience and safety are improved by avoiding travel to medical offices, but device complexity increases making deployment and operation difficult
Solution Approach 1:
The patent introduces a cloud-based monitoring system that acts as an intermediary between the complex medical device and the patient. The system includes a monitoring module that automatically tracks device operation and patient vitals, and a communication module that connects to healthcare providers, eliminating the need for patients to directly manage device complexity while maintaining safety and convenience
2Reliability
If proper training is provided for complex medical devices, then correct operation and safety are improved, but training time and resource requirements increase
Solution Approach 1:
The monitoring system performs self-service by automatically monitoring device operation and patient vitals without requiring patient intervention or training. The system autonomously detects issues, tracks usage patterns, and communicates with healthcare providers, ensuring reliable operation while eliminating training time requirements for complex device management
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the device and patient vitals are automatically collected, analyzed, and used to adjust device operation or alert healthcare providers. This automated feedback mechanism ensures correct operation without requiring patients to understand or manage the complex feedback processes themselves
3Reliability
If real-time monitoring and analysis systems are implemented, then patient safety and issue detection are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a cloud-based monitoring system that acts as an intermediary between the complex medical device and the patient. The system includes a monitoring module that automatically tracks device operation and patient vitals, and a communication module that connects to healthcare providers, eliminating the need for patients to directly manage device complexity while maintaining safety and convenience
Data Source
AI summary
Methods and systems for analyzing care data are described. The method includes building a virtual model of a physical medical device. Training is provided to a patient associated with the physical medical device to properly use the physical medical device by manipulating the virtual model. First care data associated with a first sensor associated with the physical medical device and second care data associated with a second sensor is received by a cloud service. The cloud service analyzes the first care data to obtain a first care data score and analyzes the second care data to obtain a second care data score. The cloud service scores, using a machine learning algorithm, the first care data score and the second care data score to obtain a combined care score. The cloud service determines whether the combined care score is greater than a threshold. The cloud service triggers an emergency procedure when it is determined that the combined care score is greater than the threshold.