Bed Frame Status Input for Adverse Condition Prediction
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Solution Overview
Problem
Current person support apparatuses, such as hospital beds and stretchers, lack effective systems for predicting the onset of adverse conditions in patients before they occur, limiting early intervention and care.
Innovation Solution
A control system that receives input signals from various sources, including physiological characteristics, user interfaces, and apparatus status, to calculate a condition score that alerts caregivers when it exceeds a predetermined threshold, allowing for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a control system integrates multiple input signals and physiological data to calculate condition scores, then prediction accuracy of adverse conditions improves, but device complexity increases
Solution Approach 1:
The system segments the monitoring function into multiple independent input channels (bed status sensors, physiological monitors, user interface inputs) that feed into a centralized control system. This allows complex prediction functionality to be built from modular, manageable components, improving prediction accuracy while keeping individual components simple and maintainable.
Solution Approach 2:
The control system is designed as a multi-functional platform that processes diverse input types (sensor data, physiological parameters, user inputs) through a unified condition score calculation algorithm. This universal approach enables accurate prediction of multiple adverse conditions using a single integrated system rather than separate specialized systems for each condition type.
2Reliability
If the system continuously monitors and calculates condition scores with multiple parameters, then reliability of adverse condition prediction improves, but use of energy increases
Solution Approach 1:
The system implements periodic updating of condition scores rather than continuous real-time calculation. The control system evaluates inputs at defined intervals, maintaining reliable prediction capability while reducing computational load and energy consumption compared to continuous monitoring and recalculation.
Solution Approach 2:
The system applies partial monitoring strategies where full condition score calculation is performed only when necessary (e.g., when threshold values are approached or specific trigger events occur). During stable periods, reduced monitoring with lower computational requirements maintains adequate prediction reliability while conserving energy resources.
Data Source
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AI summary
A method of predicting the onset of an adverse condition comprising receiving a first input signal from an input, receiving a second input signal corresponding to a physiological characteristic of a person supported on a person-support structure, calculating a condition score as a function of the first input signal on the physiological characteristic, and alerting a caregiver if the condition score is greater than a predetermined threshold, wherein at least one of the physiological characteristic and the predetermined threshold is scaled as a function of the first input signal, wherein the first input signal includes information corresponding to the status of the bed frame.