Medical Analyzer Notification Modeling for Predictive Maintenance
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Solution Overview
Problem
Managing and maintaining a large number of Point of Care (POC) analytical devices is challenging due to the overwhelming number of automated notifications they generate, making it difficult to track their status and predict maintenance needs accurately.
Innovation Solution
A computer-implemented method that processes automatic notifications from POC devices to infer their condition by applying identified characteristics to a model, generating notifications for maintenance tracking and predicting future needs, and providing a graphical user interface for users to take action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated notifications are generated for each POC device event, then device status monitoring is enabled, but the volume of notifications becomes overwhelming and difficult to manage
Solution Approach 1:
The patent combines multiple individual device notifications into aggregated group notifications. Instead of processing each notification separately, the system groups notifications by device type, location, or priority level, reducing the total number of notifications that need to be managed while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent creates simplified copy representations of device states rather than processing raw notification data. By generating condensed status summaries that replicate essential device information in a standardized format, the system reduces notification complexity while preserving monitoring capabilities.
2Loss of information
If all automatic notifications are processed individually, then complete device information is captured, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the notification processing workflow into distinct phases: aggregation phase where notifications are grouped, analysis phase where patterns are identified, and response phase where actions are triggered. This segmentation allows parallel processing of multiple notifications simultaneously, reducing overall processing time while maintaining information completeness.
Solution Approach 2:
The patent performs preliminary aggregation and filtering of notifications before detailed analysis. By pre-processing notifications to identify and group similar events, the system reduces the computational burden of subsequent analysis while ensuring no critical information is lost.
3Ease of operation
If traditional monitoring methods are used for POC devices, then simple device status tracking is achieved, but predictive maintenance capability is insufficient
Solution Approach 1:
The patent implements feedback loops where device notification patterns are continuously analyzed and fed back into the system to refine predictive models. By monitoring changes in notification frequency, type, and timing over time, the system learns device behavior patterns and improves predictive maintenance accuracy while maintaining ease of operation.
Solution Approach 2:
The patent performs preliminary analysis of notification patterns to identify early signs of device degradation before actual failures occur. By detecting subtle changes in device behavior through pattern recognition, the system enables proactive maintenance planning without requiring complex real-time monitoring.
Data Source
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AI summary
A computer implemented method for inferring a condition of at least one analytical device (P1A-P7A, P1B-P7B) based on at least one automatic notification received over a network (21) from the analytical device. The method comprises receiving, at a data processing agent (40), at least one automatic notification from at least one analytical device, processing, at the data processing agent (40), the at least one automatic notification, to thus identify one or more characteristics of the at least one automatic notification from the at least one analytical device, inferring, at the data processing agent (40), the condition of the at least one analytical device, by applying the one or more identified characteristics to a model; and generating, at the data processing agent (40), a notification reporting the inferred condition of the analytical device.