Service Work Order Data Quality via Neural Network Feedback
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Solution Overview
Problem
Diagnostic medical imaging systems face sub-optimal results and downtime due to component failures, exacerbated by incomplete, inaccurate, or insufficiently detailed Service Work Order (SWO) reports that hinder the development and validation of predictive and proactive diagnostic models.
Innovation Solution
A system that uses artificial neural networks (ANNs) and natural language processing (NLP) to analyze SWO reports in real-time, providing completeness scores and missing information feedback to service engineers, enhancing the quality of SWO reports and enabling better data mining for diagnostic model training and maintenance decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If SWO reports are used to mine training data for diagnostic models, then model development can proceed, but the low quality (incomplete, inaccurate, insufficiently detailed) of SWO report contents undermines the reliability of the training data
Solution Approach 1:
The system applies automated analyses to SWO reports and provides feedback to service engineers about missing or inaccurate information. This feedback loop enables real-time quality improvement of SWO reports, ensuring that training data mined from these reports meets reliability standards while maintaining productivity in model development.
Solution Approach 2:
The system enables service engineers to self-correct and improve their own SWO report quality by providing them with automated analysis results and guidance. This self-service approach allows engineers to immediately address deficiencies in their reports, improving overall data quality without requiring external intervention for each report.
2Productivity
If service engineers manually complete SWO reports without guidance, then report completion speed is maintained, but the completeness and accuracy of reported information deteriorates
Solution Approach 1:
The system provides automated feedback to service engineers about missing information in SWO reports while they are completing them. This real-time feedback enables engineers to maintain their workflow speed while ensuring that all necessary information is captured, preventing information loss without significantly impacting completion speed.
Solution Approach 2:
The system performs preliminary analysis of SWO reports as they are being completed and identifies missing information before the report is finalized. This preliminary action allows service engineers to address completeness issues during the reporting process itself, rather than requiring later corrections or re-submissions.
3Reliability
If automated analyses are applied to all SWO reports to detect missing information, then data quality improves, but processing time and computational resources increase
Solution Approach 1:
The system applies automated analyses selectively or in a prioritized manner rather than uniformly to all SWO reports. By focusing analysis efforts on reports that are most critical for model training or that show signs of incomplete information, the system maintains high data quality standards while minimizing unnecessary processing time for already-high-quality reports.
Data Source
AI summary
A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a service work order (SWO) method (200). The method includes via a user interface (UI) (140), receiving entry of a SWO report (136); applying at least one automated analysis to the SWO report to detect information missing from the SWO report and/or to generate a completeness score (138) for the SWO report; via the UI, providing an indication (142) of the information missing from the SWO report and/or the completeness score for the SWO report; and storing the SWO report in a SWO database (111).