Diagnostic Data Qualification for Unmanaged Medical Devices
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Solution Overview
Problem
Existing systems face challenges in managing diagnostic data collection from unmanaged devices, which lack consistent quality control and maintenance, leading to potential inaccuracies and security risks in diagnosis and treatment.
Innovation Solution
A system and method for registering and qualifying unmanaged devices, performing capability tests, and rating diagnostic data based on hardware performance to ensure data quality and security, enabling reliable data collection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If diagnostic data is collected from unmanaged devices, then the quantity of diagnostic data is increased, but the reliability of the diagnostic data deteriorates due to lack of quality control and maintenance
Solution Approach 1:
The system performs preliminary qualification testing of data collection hardware before allowing unmanaged devices to contribute diagnostic data. Capability tests are conducted to establish performance baselines, and devices are registered with certified hardware identifiers before data collection begins. This preliminary action ensures that only devices meeting minimum quality thresholds can contribute data, resolving the contradiction between increasing data quantity and maintaining reliability.
Solution Approach 2:
The system continuously monitors diagnostic data quality by comparing data from unmanaged devices against established performance standards and managed device benchmarks. Quality metrics are fed back to the registration system, which can update capability assessments and adjust which devices are permitted to contribute data. This feedback loop maintains data reliability while allowing maximum participation from unmanaged devices.
2Measurement precision
If capability tests are performed on unmanaged devices, then the measurement precision of hardware performance is improved, but the complexity of the data collection system increases
Solution Approach 1:
The capability testing system is segmented into modular test suites that can be independently executed and evaluated. Each test module assesses a specific hardware capability (e.g., sensor accuracy, data transmission integrity), and results are compiled into a composite device profile. This segmentation allows precise measurement of individual hardware aspects without requiring a monolithic complex testing framework.
Solution Approach 2:
The system changes testing parameters dynamically based on device type, usage patterns, and observed performance. Test frequency, rigor, and specific measurement parameters are adjusted according to the device's registered capabilities and historical data quality. This parameter adaptation maintains high measurement precision while reducing unnecessary testing complexity for well-performing devices.
3Manufacturing precision
If data is filtered based on quality standards, then the purity of diagnostic data is improved, but the loss of information increases as some data may be excluded
Solution Approach 1:
The system applies quality filtering at the level of individual data elements and devices rather than uniformly across all data. Each data point is evaluated against the specific capability standards of the contributing device, and filtering decisions are made locally based on device-specific performance characteristics. This allows high-quality data from capable devices to be retained while excluding only specific low-quality data points, minimizing information loss.
Solution Approach 2:
The system implements partial filtering by applying quality standards selectively to data elements that meet specific criteria. Rather than excluding all data from unmanaged devices or applying uniform strict filtering, the system applies quality thresholds only where necessary based on device capability assessments. This partial action maintains data purity for critical measurements while preserving supplementary information that may have lower precision requirements.
Data Source
AI summary
Methods and systems for managing collection of diagnostic data are disclosed. To collect diagnostic data, unmanaged devices may be used. The unmanaged devices may be registered with a data management system. During the registration process, procedures for processing diagnostic data from the unmanaged devices may be established. The procedures may be established based on data collection performance of the unmanaged devices. The procedures may reduce the likelihood of use of diagnostic data that is unreliable. Once obtained, the diagnostic data may be rated for different uses based on the performance of the hardware used to obtain the diagnostic data.