Alert Annotation Interface for Actionable Alarm Classification
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Solution Overview
Problem
Medical workers experience high burden due to the frequency of non-actionable alarms from medical devices, leading to 'alarm fatigue', which existing systems struggle to efficiently discriminate actionable from non-actionable alerts.
Innovation Solution
A system and method that allows medical workers to input annotations through an interface to classify alerts as actionable or non-actionable, facilitating the collection of high-quality training data for an inference engine, reducing the need for extensive manual discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical workers manually check and manage all alerts issued from medical devices, then the accuracy of alert discrimination is maintained, but the burden on medical workers increases significantly leading to alarm fatigue
Solution Approach 1:
The patent introduces an inference engine as an intermediary between medical devices and medical workers. This engine automatically discriminates actionable alerts from non-actionable ones using machine learning models, reducing the burden on medical workers while maintaining discrimination accuracy. The engine acts as a mediator that processes alerts and provides prioritized information to workers.
Solution Approach 2:
The system enables self-service by allowing the inference engine to autonomously analyze and classify alerts without requiring manual intervention from medical workers for each alert. The engine independently determines which alerts require attention, enabling the system to serve itself in the alert management process.
2Ease of operation
If an inference engine with machine learning is used to discriminate actionable from non-actionable alarms, then the burden on medical workers is reduced, but an enormous amount of training data is required
Solution Approach 1:
The patent implements preliminary action by collecting and storing alert data with annotations in advance through a data collection system. Medical workers provide annotations during normal operation, and this accumulated data is prepared beforehand for training the inference engine. This preliminary data collection eliminates the need for extensive manual data preparation later.
Solution Approach 2:
The system incorporates feedback mechanisms where medical workers annotate alerts, and this feedback is used to iteratively improve the inference engine's performance. The annotations from workers provide continuous feedback that refines the machine learning model, reducing the amount of initial training data needed while improving accuracy over time.
3Reliability
If the frequency of non-actionable alarms is high, then comprehensive monitoring coverage is achieved, but alarm fatigue increases due to the need to manage numerous low-urgency alerts
Solution Approach 1:
The patent extracts non-actionable alerts from the overall alert stream using the inference engine. By identifying and separating non-actionable alerts based on patterns in the data, the system removes these low-urgency alerts from the workflow that requires medical worker attention, while maintaining comprehensive monitoring of all alerts.
Solution Approach 2:
The system applies local quality by providing different levels of attention and processing to different types of alerts. Actionable alerts receive immediate attention and detailed analysis, while non-actionable alerts are automatically filtered or marked for later review. This differential treatment optimizes productivity by focusing resources on critical alerts.
Data Source
AI summary
A medical device is configured to issue an alert. An annotation assigning device is configured to provide an input user interface arranged to input managing information indicating whether the alert is an actionable alarm or a non-actionable alarm. The annotation assigning device is configured to assign an annotation corresponding to the managing information to the alert.