Predictive Work Orders From Medical Device Event Logs
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Solution Overview
Problem
Existing medical device maintenance schedules lack proactive detection of potential issues, leading to costly and time-consuming repairs, and there is a need for efficient evaluation systems to generate work orders for timely maintenance.
Innovation Solution
A system comprising an event detection controller and network access devices that analyze log data from medical devices to detect events like air bubble issues in CT scanners, using multiple processing strategies to generate work orders based on threshold criteria, and prioritize technician notifications based on severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard maintenance schedules are used for medical devices, then maintenance is performed regularly, but potential issues are not detected proactively leading to costly repairs and downtime
Solution Approach 1:
The system performs preliminary detection of potential device issues by continuously monitoring log data and evaluating events before they cause actual device failure. This proactive approach allows maintenance to be scheduled in advance, preventing unplanned downtime and costly emergency repairs.
Solution Approach 2:
The system establishes a feedback loop by continuously collecting device log data, evaluating events against predefined criteria, and generating work orders when issues are detected. This closed-loop feedback mechanism enables ongoing monitoring and timely intervention to maintain device reliability.
2Measurement precision
If multiple processing strategies are evaluated to detect events accurately, then detection precision improves, but system complexity increases
Solution Approach 1:
The system divides event detection into multiple independent processing strategies, each evaluating specific event types or criteria. This segmentation allows each strategy to focus on particular detection tasks, improving overall detection precision while maintaining manageable complexity through modular design.
Solution Approach 2:
The system implements multiple processing strategies that may independently evaluate the same data, using an excessive approach to ensure comprehensive event detection. This redundancy improves detection accuracy by cross-validating results across multiple evaluation methods.
Data Source
AI summary
Systems for evaluating a plurality of medical devices over an at least one network are provided. The systems may include an event detection controller, a plurality of network access devices, and a plurality of technician mobile computing devices. Each network access device may be operatively coupled to a respective one of the plurality of medical devices. Each network access device may receive log data from the respective medical device and process the log data to provide a collection of device data to the event detection controller over the at least one network. The event detection controller may evaluate the collection of device data to identify a plurality of events and generate one or more work orders based on the plurality of events. An exemplary event may be an air bubble event for a CT scanner.