Medical Device Diagnostic Filter Recommendation for Faster Service Actions
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Solution Overview
Problem
Remote service engineers face challenges in selecting appropriate filters for historical service case databases, leading to sub-optimal and arduous search processes for diagnosing medical device malfunctions, which can result in incorrect or costly service actions.
Innovation Solution
An automated system that generates and simulates candidate filter value combinations based on the context of the current service case, recommending optimal filter values and actions to retrieve relevant historical service cases, thereby improving search accuracy and reducing the number of searches required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple manual searches are performed to find appropriate service actions, then the chance of selecting the right service action increases, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary analysis of the service context and pre-generates optimal filter combinations before the engineer needs to search. By analyzing the current service case context upfront and pre-computing the best search filters, the system eliminates the need for multiple iterative manual searches, providing the correct service actions in a single query.
Solution Approach 2:
The system introduces an intelligent intermediary layer between the engineer and the historical service case database. This intermediary automatically analyzes the service context, selects appropriate filters, and retrieves relevant service actions, replacing the manual trial-and-error search process with automated intelligent retrieval.
2Ease of operation
If engineers manually configure search filters, then they can control the search parameters, but the complexity of the task increases and errors occur
Solution Approach 1:
The system performs self-service by automatically analyzing the service context and configuring the optimal search filters without engineer intervention. The system autonomously selects filter parameters, combines them appropriately, and executes the search, completely eliminating the manual filter configuration task for the engineer.
Solution Approach 2:
The system dynamically changes filter parameters based on the analyzed service context. Instead of using fixed or manually selected parameters, the system automatically adjusts and optimizes filter parameters according to the specific characteristics of the current service case, achieving both simplicity and accuracy.
3Measurement precision
If comprehensive filters are applied to search historical cases, then the search results become more accurate, but the number of possible filter combinations explodes
Solution Approach 1:
The system applies local quality by selecting only the specific filter parameters that are relevant to the current service context, rather than applying all possible filters uniformly. It identifies and activates only the necessary filters based on the particular characteristics of the service case, achieving precision without combinatorial explosion.
Solution Approach 2:
The system dynamically determines which filter parameters to apply based on the service context analysis. It changes the active set of parameters from a fixed comprehensive list to a context-dependent subset, maintaining search precision while avoiding the complexity of evaluating all possible filter combinations.
4Reliability
If engineers focus on technical aspects of service actions, then they can identify possible solutions, but they may miss important information about frequency and cost
Solution Approach 1:
The system provides a unified interface that simultaneously presents multiple dimensions of service action information including technical details, historical frequency, and cost metrics. Instead of requiring separate analyses for each aspect, the system integrates all these functions into a single comprehensive presentation, making all information equally accessible.
Solution Approach 2:
The system incorporates feedback from historical service data by automatically analyzing and presenting frequency and cost information alongside technical solutions. This feedback loop provides engineers with evidence-based insights about which service actions have been successful in similar cases and their associated costs, enabling more informed decision-making.
Data Source
AI summary
A database stores summaries of historical service cases. A request for assistance is received for a current service case for a medical device. A context of the current service case is identified based at least on the received request. Candidate filter value combinations are generated for a plurality of filters for use in searching the summaries of historical service cases. The candidate filter combinations are generated based on the identified context of the current service case. The candidate filter value combinations are simulated by filtering the database using the candidate filter combinations. A recommended combination of filter values or a recommended service action is identified based on the simulating. On a display device of an electronic processing device operable by a service engineer (SE), the recommended combination of filter values or recommended service action is output.