Acoustic Alarm Categorization for Medical Device Prioritization
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Solution Overview
Problem
The lack of communication between medical devices leads to issues such as lack of context and situational awareness, alarm fatigue, inefficient prioritization and response, and increased risk of missed or delayed alarms, compromising patient safety and increasing stress on healthcare providers.
Innovation Solution
A computer-implemented method that acoustically monitors a medical environment to identify and categorize audible alarms based on type, severity, duration, magnitude, and frequency, using AI models to set a baseline and generate reports, facilitating communication between devices from different vendors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple medical devices are deployed to monitor patient conditions, then patient safety and monitoring coverage are improved, but alarm fatigue and difficulty in prioritizing alarms worsen
Solution Approach 1:
The patent introduces an acoustic monitoring system as an intermediary that captures, processes, and centralizes alarms from multiple medical devices. The system uses microphones to acoustically monitor alarm signals, AI models to categorize and prioritize them, and a centralized interface to present organized alarm information to healthcare providers, thereby reducing alarm fatigue while maintaining comprehensive monitoring coverage
Solution Approach 2:
The patent replaces the traditional mechanical/electronic direct communication between medical devices with an acoustic-based monitoring system. Instead of devices communicating through standardized electrical interfaces, the system uses acoustic signal capture, processing, and analysis to monitor and manage alarms across multiple devices, enabling universal monitoring without requiring device-specific integrations
2Productivity
If devices communicate effectively with each other, then alarm prioritization and response efficiency are improved, but device compatibility and integration complexity worsen
Solution Approach 1:
The acoustic monitoring system provides a universal interface that can monitor alarms from any medical device regardless of manufacturer or communication protocol. The system uses acoustic signal processing and AI-based categorization to handle diverse alarm types uniformly, enabling effective alarm management across heterogeneous device ecosystems without requiring device-specific integrations
Solution Approach 2:
The system acts as an intermediary layer between diverse medical devices and healthcare providers. By capturing acoustic alarm signals and processing them through a centralized AI-driven platform, the system enables effective alarm prioritization and response without requiring direct communication or integration between individual devices
3Reliability
If acoustic monitoring is implemented to capture all alarms, then alarm detection completeness is improved, but false alarms and noise interference worsen
Solution Approach 1:
The system implements feedback mechanisms where AI models continuously analyze acoustic signals, learn from patterns, and refine alarm detection thresholds. The categorization process provides feedback to distinguish true alarms from noise, and the system can adjust sensitivity based on environmental conditions and device-specific characteristics, reducing false alarms while maintaining detection completeness
Solution Approach 2:
The system dynamically adjusts detection parameters such as sensitivity thresholds, frequency ranges, and time windows based on the specific alarm source and environmental conditions. By changing these parameters adaptively, the system optimizes the balance between detecting all true alarms and filtering out false alarms and background noise
Data Source
AI summary
A computer-implemented method, computer program product and computing system for: acoustically monitoring a medical environment to generate an acoustic signal indicative of audio within the medical environment; processing the acoustic signal to identify one or more audible alarms within the medical environment; categorizing the one or more audible alarms, thus defining categorized alarms; and processing the categorized alarms.