Automatic Patient Device Association via Image Shape Analysis
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Solution Overview
Problem
Current systems for associating medical devices with patients in hospitals are error-prone, cumbersome, and inefficient, often requiring manual barcode scanning, inaccurate RTLS, or time-consuming manual entry of serial numbers, which diverts nursing staff from primary patient care.
Innovation Solution
A system utilizing video image processing to automatically identify medical devices and patients by analyzing image data to match device shapes with templates, supplemented by context information from RTLS and ADT systems, to accurately associate devices with patients without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual barcode scanning is used to associate devices with patients, then device-patient association can be established, but nursing staff time and manual labor increase
Solution Approach 1:
The system enables automatic self-association of devices with patients through computer vision and machine learning. The camera captures images of the patient bed area, the processor automatically identifies the patient and device, and establishes the association without requiring nurse intervention. This transforms a manual service into an autonomous system that performs the association task itself.
Solution Approach 2:
The patent replaces the mechanical manual barcode scanning process with an automated optical recognition system. Instead of nurses physically scanning barcodes with handheld readers, the system uses cameras and image processing algorithms to automatically detect, identify, and associate devices with patients, substituting mechanical manual operations with automated optical and computational processes.
2Extent of automation
If RTLS is used to determine device-patient association through proximity, then automation is achieved, but measurement precision is insufficient to distinguish between patients in the same room
Solution Approach 1:
The system employs local quality analysis by focusing the camera specifically on the patient bed area rather than capturing the entire room. The processor analyzes the local region where the patient is positioned, examining details such as bed characteristics, patient location relative to the bed, and device proximity to the patient within this localized area. This local focus enables precise identification even when multiple patients are present in the same room.
Solution Approach 2:
The patent transitions from RTLS's coarse spatial proximity measurement to a multi-dimensional analysis approach. The system captures images with spatial coordinates, identifies patients and devices within the image frame, determines their relative positions, and analyzes the dimensional relationships between objects. This dimensional analysis provides precise differentiation between patients and devices based on their spatial relationships within the captured image data.
3Loss of information
If video cameras are used to view patients remotely, then patient monitoring is enabled, but automatic device-patient association is not achieved
Solution Approach 1:
The system makes the video camera and processing infrastructure universal by enabling it to perform multiple functions: patient monitoring, device detection, device identification, and automatic association. The same camera and processor that monitor patient status also automatically identify medical devices, determine device-patient relationships, and update association records, eliminating the need for separate dedicated association systems.
Solution Approach 2:
The patent merges the patient monitoring function with the device association function into a single integrated system. The camera captures images serving dual purposes, the processor performs both patient identification and device recognition, and the same data processing pipeline establishes both monitoring records and device-patient associations. This consolidation combines previously separate functions into one unified automated system.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Automatically and accurately associates medical devices with patients, reducing errors and administrative burdens, allowing nurses to focus on patient care by providing a reliable and efficient method for data linkage.
Implementation Method 1
An image data processor analyzes the acquired data representing the image to identify the medical device type by, analyzing the acquired data to determine a shape of the medical device, comparing the determined shape of the medical device with predetermined template shapes of known device types
Data Source
AI summary
A system associates a patient and patient identifier with a medical device and includes an interface. The interface acquires data representing an image of a patient in a care setting and showing a medical device in the vicinity of the patient and acquires data identifying the patient. An image data processor analyzes the acquired data representing the image to identify the medical device type by, analyzing the acquired data to determine a shape of the medical device, comparing the determined shape of the medical device with predetermined template shapes of known device types and identifying the medical device type in response to the shape comparison indicating a shape match. A data processor associates the identified medical device type with the data identifying the patient. An output processor initiates generation of data indicating an association of the identified medical device type with the data identifying the patient.