Patient Support Apparatus Machine Learning Control
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Solution Overview
Problem
Existing patient support apparatuses lack the ability to dynamically adapt and improve their operations over time, failing to predict and prevent events such as bed sores, patient exits, and ventilator-associated pneumonia, and requiring manual selection of settings by users.
Innovation Solution
Integration of machine learning algorithms and sensors in patient support apparatuses that gather data on user preferences and environmental conditions to automatically select optimal settings and predict future events, such as using a neural network to analyze sensor data from multiple sources to improve algorithms and alert sensitivity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual setting selection is used in patient support apparatuses, then user control and customization are maintained, but operational efficiency decreases and user burden increases
Solution Approach 1:
The system automatically selects settings by monitoring user interactions and learning preferences over time, eliminating the need for manual setting selection while maintaining user control through implicit preference capture
Solution Approach 2:
The system continuously monitors user adjustments and manual selections to learn and adapt settings automatically, creating a feedback loop where user behavior informs future automatic settings
2Reliability
If traditional algorithms with fixed sensor collections are used, then system simplicity is maintained, but predictive capabilities for events like bed sores and patient exits are insufficient
Solution Approach 1:
The system dynamically adapts by continuously learning from new data and adjusting its predictive models over time, transitioning from static fixed algorithms to dynamic adaptive systems that improve predictions for events like bed sores and patient exits
Solution Approach 2:
The system performs preliminary analysis of sensor data patterns to predict future events before they occur, such as predicting patient exit attempts or bed sore risks before they manifest, enabling preventive actions
3Productivity
If machine learning algorithms are integrated to automatically select settings, then operational efficiency and user experience improve, but device complexity increases
Solution Approach 1:
The machine learning system serves itself by automatically improving its algorithms through continuous data collection and analysis, reducing the need for manual configuration and maintenance while enhancing operational efficiency
Data Source
AI summary
A patient support apparatus, such as a bed, stretcher, cot, or the like, includes a frame, a support surface, a control, a controller, and a transceiver. The patient support apparatus employs one or more machine learning techniques to perform one or more of the following: automatically implement one or more user-preferred settings, automatically predict the occurrence of one or more events based on analyses of prior events, and/or automatically improve one or more algorithms based on analyses of additional sensor data. The machine learning techniques may be implemented onboard the patient support apparatus and/or may be implemented at a remote computer device (e.g. a server) that collates and analyzes data from multiple patient support apparatuses, and then sends the results of the analyses back to the patient support apparatuses.