Image-Based Medical Device Pairing to Reduce Manual Association Errors
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Solution Overview
Problem
In clinical environments, devices often fail to accurately associate patient data with the correct individual, leading to missed parameters and erroneous charting, and manual intervention is required to manage increasing amounts of patient-related information, making it difficult for care providers to efficiently utilize device data.
Innovation Solution
A system that uses image analysis to identify and associate medical devices with patients, automatically controlling device settings and actions based on detected events, reducing the need for manual input and preventing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual association of devices with patients is used, then device pairing can be established, but it increases manual effort and time consumption for care providers
Solution Approach 1:
The system enables devices to automatically associate with patients through image recognition technology. The imaging system captures images of the clinical setting, identifies both the patient and the device, and automatically creates the association without requiring manual intervention from care providers. This self-service approach eliminates the time-consuming manual device pairing process while maintaining accurate patient-device associations.
2Productivity
If devices automatically chart patient parameters, then data collection efficiency improves, but incorrect patient association leads to erroneous charting
Solution Approach 1:
The system incorporates continuous verification through image recognition to ensure accurate patient-device association. The imaging system periodically captures images and verifies that the device is still associated with the correct patient. This feedback mechanism allows the system to maintain high data collection efficiency while preventing erroneous charting by detecting and correcting association errors in real-time.
3Loss of information
If care providers manually review device information, then patient data can be monitored, but the increasing amount of information makes it difficult to efficiently utilize device data
Solution Approach 1:
The system introduces an automated image recognition intermediary that bridges the gap between devices and care providers. This intermediary automatically captures images, identifies patients and devices, associates them correctly, and monitors patient parameters. By placing this intelligent intermediary in the information flow, the system reduces the burden on care providers while ensuring accurate patient data monitoring and association.
4Reliability
If more devices are deployed in clinical environments, then patient monitoring capability improves, but manual management of device associations becomes more difficult
Solution Approach 1:
The system enables automatic self-service device association through image recognition. Each device independently identifies the correct patient through image capture and analysis, eliminating the need for manual management of device associations. This approach allows for the deployment of multiple devices across clinical environments while maintaining ease of operation, as each device autonomously manages its own patient association without requiring manual intervention.
Data Source
AI summary
An example method includes capturing images using a camera and detecting a medical device in a first image among the images. A request is transmitted to the medical device. Based on transmitting the request, the example method includes determining that the medical device has output a chirp signal in a second image among the images. Based on the chirp signal, the method includes causing the medical device to perform an action by transmitting a control message to the medical device.