Alert Timeline Visualization for Medical Systems
Solution Overview
Problem
Existing Picture Archiving and Communication Systems (PACS) face challenges in efficiently monitoring and visualizing alerts, particularly due to the large number of alerts, which can lead to difficulties in identifying individual issues or long-term trends, and in determining the root cause of system malfunctions.
Innovation Solution
A method for alert monitoring that involves receiving timestamped alerts from medical systems, segmenting the displayed time span into discrete time units, generating a timeline representing the alerts over the displayed time span using a two-dimensional grid of blocks, and color-coding each block based on the fraction of time it represents that the alert is critical, allowing for dynamic adjustment based on zoom level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If one zooms in to a detailed level to see individual critical intervals, then individual alerts become clearly visible, but the overview of trends is lost and excessive scrolling is required
Solution Approach 1:
The timeline is segmented into discrete time units (e.g., hours, days, weeks) that are dynamically selected based on the displayed time span. Each time unit is represented by a block that can be independently analyzed. This segmentation allows the system to provide both detailed views (when zoomed in) and overview views (when zoomed out) without requiring excessive scrolling, as the appropriate time unit granularity automatically adapts to the current zoom level.
Solution Approach 2:
The time unit granularity is dynamic and automatically adjusts based on the displayed time span. When a user zooms in, the system switches to finer time units (e.g., from days to hours to minutes) to maintain appropriate detail visibility. When zoomed out, coarser time units are used to maintain overview capability. This dynamic adaptation eliminates the need for manual scrolling through fixed granularities.
2Measurement precision
If one zooms in to see individual critical intervals, then detail visibility improves, but the overall trend overview deteriorates
Solution Approach 1:
The patent introduces a temporal dimension to the visualization by organizing blocks in sequence along the time axis. Each block represents a discrete time unit and can be visually distinguished by its position in the sequence. This temporal arrangement allows users to simultaneously perceive individual intervals (through block details) and overall trends (through the sequence pattern), effectively adding a time-based dimension that resolves the contradiction between detail and overview.
Solution Approach 2:
By segmenting the continuous timeline into discrete, visually distinct blocks, the system enables users to see both individual intervals and their arrangement in sequence. The block-based representation maintains the temporal relationships necessary for trend analysis while providing clear boundaries for individual interval inspection.
3Reliability
If multiple critical intervals start simultaneously for multiple parameters, then comprehensive monitoring is achieved, but determining the root cause becomes difficult
Solution Approach 1:
Different alert sources or parameter types are represented by different colors in the block visualization. When multiple critical intervals start simultaneously, their distinct colors allow for immediate visual differentiation. This color-coding system enables users to quickly identify which parameters are affected and potentially spot patterns or correlations that would indicate the root cause, maintaining comprehensive monitoring while improving detectability.
Solution Approach 2:
Each block is assigned specific visual properties (such as color, shading, or icons) that encode information about the alert source, severity, or type. This local differentiation allows users to quickly assess the nature of each critical interval without having to examine detailed data, making it easier to identify potential root causes among multiple simultaneous alerts.
Data Source
AI summary
An alert monitoring method includes receiving alerts generated by one or more medical systems, each alert being timestamped and associated to the medical system that generated the alert; segmenting a displayed time span into discrete time units whose size depends on the displayed time span; generating a timeline representing the alerts over the displayed time span, the timeline comprising a two-dimensional grid of blocks with each block having a corresponding discrete time unit and a corresponding alert source for the one or more medical systems, and each block being color coded based on a fraction of the corresponding discrete time unit over which the corresponding alert is critical; displaying, on a display device, the timeline representing the alerts over the displayed time span; and in response to receiving a user input adjusting the displayed time span, displaying an updated timeline representing the alerts over the updated displayed time span.