Selective Sensor Monitoring for Patient Elopement Risk
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Solution Overview
Problem
Existing patient monitoring systems in care settings are inefficient in preventing patient elopement, often relying on manual oversight, generating excessive data, consuming significant resources, and raising privacy concerns, with wearable sensors having limited battery life due to constant communication demands.
Innovation Solution
A machine learning-based system that predicts elopement likelihood, selectively enables sensors for at-risk patients, and implements targeted interventions using sensor data to enhance accuracy, reduce resource consumption, and improve privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor devices are continuously activated to monitor patient movement, then patient safety and elopement detection are improved, but device battery life deteriorates due to constant communication demands
Solution Approach 1:
The system performs preliminary risk assessment using machine learning models to identify patients at risk of elopement before activating sensors. This preliminary action allows the system to selectively activate sensors only for at-risk patients, avoiding continuous activation for all patients and thereby preserving battery life while maintaining safety for vulnerable individuals.
Solution Approach 2:
The system applies different monitoring strategies to different patients based on their individual risk profiles. High-risk patients receive continuous sensor monitoring, while low-risk patients have sensors deactivated. This localized quality approach ensures safety for those who need it most while conserving battery resources across the overall patient population.
2Reliability
If sensor devices are continuously activated to track all patients, then complete monitoring coverage is achieved, but resource consumption and data processing burden increase significantly
Solution Approach 1:
The system implements partial monitoring by activating sensors only for patients who exceed a certain risk threshold, rather than monitoring all patients continuously. This partial action approach maintains adequate monitoring coverage for at-risk patients while significantly reducing resource consumption and data processing requirements compared to universal continuous monitoring.
Solution Approach 2:
The patient population is segmented into different risk categories using machine learning assessment. Sensors are activated only for segments identified as high-risk, while low-risk segments have sensors deactivated. This segmentation strategy ensures monitoring coverage is concentrated where most needed while reducing overall system resource consumption.
3Reliability
If manual oversight and security camera footage are used to detect elopement, then comprehensive monitoring is provided, but significant manpower and time resources are required
Solution Approach 1:
The system replaces manual mechanical oversight (security guards reviewing camera footage) with an automated electronic monitoring system using sensors and machine learning algorithms. This substitution eliminates the need for continuous human review while maintaining or improving elopement detection capability, thereby reducing manpower requirements and time loss.
Solution Approach 2:
The monitoring system performs self-assessment through machine learning models that automatically analyze sensor data and identify elopement risks without human intervention. The system serves itself by autonomously detecting potential elopement events and alerting appropriate personnel only when necessary, eliminating the need for continuous manual surveillance.
Data Source
AI summary
Techniques for improved sensor monitoring using machine learning are provided. An elopement likelihood indicating a probability of elopement for a patient in a care setting is generated by processing first patient data using a trained machine learning model. One or more sensor devices are enabled in response to determining that the elopement likelihood exceeds a threshold. Second patient data is collected for the patient using the enabled one or more sensor devices. An intervention for the patient is selected based on the second patient data, and the intervention is enacted.