Personalized Alarm Settings via Pre-admission Wearable Data
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Solution Overview
Problem
Patients admitted to healthcare facilities often experience elevated or altered vitals due to their condition, leading to improperly set alarm settings and false alarms in monitoring devices.
Innovation Solution
A monitoring device that receives health data from wearable smart devices prior to admission to determine a patient's normal resting state, allowing for personalized adjustment of alarm settings based on stabilized physiological values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alarm settings are based on standard thresholds for admitted patients, then alarm coverage is broad, but false alarms increase due to elevated vitals from admission condition
Solution Approach 1:
The system collects and analyzes health data from wearable devices before patient admission to establish baseline physiological values. This preliminary action enables the alarm system to be pre-configured with personalized thresholds that account for the patient's normal state, preventing false alarms triggered by admission-related vitals changes.
Solution Approach 2:
The system dynamically adjusts alarm threshold parameters based on the patient's individual baseline physiological data obtained from pre-admission wearable device measurements. Instead of using fixed standard thresholds, the alarm parameters are customized to reflect the patient's normal resting state, thereby improving reliability without sacrificing adaptability.
2Reliability
If alarm settings are personalized based on pre-admission data, then false alarms are reduced, but system complexity increases due to data collection and processing requirements
Solution Approach 1:
The system uses wearable devices as intermediary components to collect pre-admission health data. These devices serve as external data sources that feed information to the monitoring system, enabling personalized alarm configuration without requiring the monitoring device itself to perform complex data collection functions, thus managing system complexity.
Solution Approach 2:
By performing data collection and baseline establishment before admission, the system reduces the computational and processing burden during critical monitoring periods. The preliminary analysis of wearable device data creates ready-to-use personalized parameters, simplifying the ongoing monitoring complexity while maintaining high alarm accuracy.
3Ease of operation
If standard alarm thresholds are used for all patients, then device operation is simple, but measurement precision decreases for individual patient baselines
Solution Approach 1:
The system automatically collects data from wearable devices and performs unsupervised analysis to determine patient-specific baseline physiological values. This self-service approach eliminates the need for manual data entry or complex configuration by operators, maintaining ease of operation while achieving high measurement precision through automated personalized baseline establishment.
Solution Approach 2:
The system dynamically adapts alarm threshold parameters based on automatically determined patient baselines from wearable data. This automated parameter customization achieves high measurement precision for individual patients without requiring manual intervention, thereby maintaining operational simplicity while improving accuracy.
Data Source
AI summary
A method for monitoring a patient includes detecting a personal device of the patient, sending an invitation to connect to the personal device, and receiving health data from the personal device when the invitation is accepted. The method further includes determining stabilized values for one or more physiological variables of the patient based on the health data, and adjusting one or more alarm settings based on the stabilized values.