Patient Support Monitoring with Condition Scoring and Caregiver Alerts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current person support apparatuses, such as hospital beds, lack effective systems for predicting adverse conditions before they occur, and there is a need for improved vital signs monitoring and movement detection to enhance patient care.
Innovation Solution
A person monitor system that includes sensors to detect patient movement and physiological characteristics, a controller to calculate a condition score, and a communication system to alert caregivers when the score exceeds a threshold, using a modified early warning score (MEWS) and incorporating sensors for heart rate, respiration rate, and temperature, along with a display for sleep quality analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are integrated into the person support apparatus to monitor physiological characteristics and movement, then the measurement precision and reliability of patient condition assessment is improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple sensors (load cells for movement detection, physiological sensors for vital signs monitoring) into a single integrated person support apparatus. The controller consolidates data from all sensors to calculate a unified condition score, merging multiple monitoring functions into one cohesive system that improves measurement precision without requiring separate standalone devices.
Solution Approach 2:
The person support apparatus is designed as a multi-functional system that simultaneously performs structural support, movement detection via load cells, physiological monitoring via integrated sensors, and condition assessment through automated scoring. This universal design allows a single device to fulfill multiple functions, reducing the need for separate equipment while maintaining high measurement precision.
2Productivity
If automated condition score calculation and alert systems are implemented, then the productivity of patient monitoring and response time to adverse conditions is improved, but the device complexity increases
Solution Approach 1:
The system performs automated condition score calculation and threshold comparison without requiring manual intervention. The controller automatically processes sensor data, computes the condition score using the specified algorithm, compares it against predefined thresholds, and triggers alerts when necessary. This self-service capability improves monitoring productivity by eliminating manual assessment steps while the control system handles complexity internally.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is constantly monitored, condition scores are recalculated in real-time, and alerts are generated when thresholds are violated. This automated feedback mechanism improves response time to adverse conditions by immediately notifying caregivers of deteriorating patient status without requiring manual checking or interpretation of multiple parameters.
3Reliability
If comprehensive sensor integration is used to detect both movement and physiological characteristics, then the reliability of adverse condition prediction is improved, but the loss of information increases due to the complexity of data integration
Solution Approach 1:
The system incorporates preliminary data processing steps where raw sensor data is normalized, filtered, and pre-processed before being fed into the condition score calculation algorithm. Movement data from load cells and physiological data from sensors are prepared in advance with appropriate weighting factors and conversion formulas established beforehand, ensuring that the integrated assessment maintains reliability while preventing information loss through systematic data preparation.
Solution Approach 2:
The system transforms diverse sensor inputs (force measurements from load cells, electrical signals from physiological sensors) into a unified condition score parameter. By changing the parameters of different data types into a common scoring framework with standardized thresholds, the system maintains reliability of adverse condition prediction while avoiding information loss that would occur from attempting to directly compare or integrate heterogeneous data formats.
Data Source
AI summary
A person monitoring system is operable to predict the onset of an adverse condition of a person. The system receives first information corresponding to a feature of a person support apparatus and second information corresponding to a physiological characteristic of the person. The system calculates a condition score as a function of the first and second information. In some instances, the system alerts a caregiver if the condition score exceeds a predetermined threshold. Alternatively or additionally, the person monitoring system alerts a caregiver when a person supported on a person support apparatus is regaining consciousness by monitoring one or more of a change in position, a heart rate, and a respiration rate. A person monitoring system that monitors a person's quality of sleep and presents information concerning the quality of sleep to one or more interested parties is also disclosed.