Cross-vendor Imaging Workflow Analysis via Anonymized Feature Patterns
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Solution Overview
Problem
Conventional radiology workflow analysis is limited by the inability to share or use modality log file data across different vendors' imaging equipment, due to potential data privacy and trade secret issues.
Innovation Solution
A system that uses sensors installed in a data security zone to collect raw data from medical imaging equipment, preprocesses this data to remove sensitive information, and generates feature patterns that can be analyzed using machine learning algorithms to identify imaging procedures without revealing patient or vendor-specific information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If modality log file data is shared across different vendors for workflow analysis, then workflow analysis capability is improved, but data privacy and trade secret protection deteriorates
Solution Approach 1:
The patent extracts only the necessary workflow information from modality log files while leaving sensitive vendor-specific implementation details and patient information behind. The extraction process isolates generic workflow patterns that can be shared across vendors without exposing proprietary algorithms or patient data, thus enabling cross-vendor analysis while protecting privacy.
Solution Approach 2:
The patent introduces an intermediary processing layer that sits between the vendor-specific log files and the cross-vendor analysis platform. This intermediary translates vendor-specific log formats into a standardized, anonymized workflow representation that preserves analytical value while removing sensitive information, enabling safe data sharing across vendor boundaries.
2Measurement precision
If detailed modality log file information is made available for cross-vendor analysis, then measurement precision is improved, but data security deteriorates
Solution Approach 1:
The patent applies different processing qualities to different parts of the log file data. Sensitive fields such as patient identifiers, vendor-specific algorithm parameters, and proprietary system configurations are heavily anonymized or removed, while generic workflow fields such as scan type, duration, and basic parameters are preserved with high fidelity. This local differentiation of data quality enables precise workflow analysis without compromising security.
3Productivity
If vendor-specific operational data is shared for workflow optimization, then productivity is improved, but loss of information increases due to data anonymization
Solution Approach 1:
The patent applies partial anonymization rather than complete anonymization of all log file data. It selectively removes only the specific portions of vendor-specific data that would compromise trade secrets or patient privacy, while retaining sufficient operational details to enable meaningful workflow analysis and optimization. This partial action approach maintains productivity benefits while minimizing information loss.
Data Source
AI summary
When acquiring detailed utilization information from imaging equipment in a cross-vendor approach, one or more sensors (16, 18, 22, 24) are positioned within a data security zone (14) in which an imaging procedure is performed. Sensor data is pre-processed on an isolated processing unit (20) to remove any sensitive information and keep a selection of features only. The resultant feature pattern is transmitted outside of the data security zone to a processing unit (28) where pattern recognition is performed on feature pattern to identify the type of imaging modality, scan, etc. being performed as well as to determine whether the scan is being performed according to schedule.