Medical Device Error Pattern Matching for Faulty Part Identification
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Solution Overview
Problem
In clinical operations, medical device outages due to faulty structural parts can be lengthy and time-critical, and existing computer-assisted methods struggle when little operating data is available, especially during the early product cycle or after device release.
Innovation Solution
A method and database system that uses machine-based algorithms and simulation models to identify faulty structural parts for exchange, leveraging test and simulation data to generate error patterns, enabling proactive and automatic identification without the need for manual input or operating data, with a self-learning system that continuously updates and expands error patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computer-assisted methods are used to identify faulty structural parts, then the identification process is automated and speeded up, but the methods fail when little operating data is available
Solution Approach 1:
The patent applies preliminary action by pre-generating error patterns and exchange data records through simulation models before actual device operation begins. The database is populated with simulated error scenarios and corresponding replacement part information during the development phase, enabling the system to function immediately upon device commissioning without requiring accumulated operating data.
Solution Approach 2:
The patent uses copying by creating simulated error patterns that replicate actual device errors without requiring real operational data. The simulation model generates virtual error scenarios and their corresponding solutions, which are stored in the database as copies of what would be observed in actual operation, allowing the identification algorithm to function with simulated rather than real data.
2Measurement precision
If manual data input and processing is used, then data accuracy can be verified, but the time required for error handling and part identification increases
Solution Approach 1:
The patent implements self-service through the identification algorithm that automatically processes error messages, queries the database for matching error patterns, and returns the appropriate exchange data record without any manual intervention. The system serves itself by autonomously completing the entire identification workflow from error detection to part specification, eliminating manual data input and processing steps.
Solution Approach 2:
The patent replaces the mechanical system of manual data processing with an automated electronic identification algorithm. The algorithm electronically queries the database, matches error patterns, and retrieves exchange data records, substituting the manual mechanical process of data verification and part identification with an automated computational system that operates at electronic speeds.
3Ease of operation
If a database is expanded with simulation data, then the system can operate immediately after product release, but the complexity of data management increases
Solution Approach 1:
The patent introduces an intermediary simulation model that acts as a mediator between the device manufacturer and the operational system. The simulation model generates standardized error patterns and exchange data records that are stored in the database, serving as an intermediary layer that simplifies data management by providing a systematic method for populating and maintaining the database without requiring complex manual data entry or processing.
Data Source
AI summary
In a method, a database system, a computer, and a medical system for the identification of faulty structural parts to be exchanged of a medical device, an identification algorithm is executed to calculate and emit an exchange data record that identifies a structural part to be exchanged, dependent on for a read-in error message from the medical device. The identification algorithm accesses a database system with a simulation model in which automatically generated error patterns (are stored in order to perform an analysis on these stored error patterns, so as to calculate the exchange data record. An error pattern is an association between the error message, an exchange data record, and an evaluation data record.