Medical Data Acquisition System Quality Rating
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Solution Overview
Problem
Medical Data Acquisition Systems (MDAS) face reliability challenges due to time-based failure metrics that do not account for clinical exam performance, leading to inefficiencies and high maintenance costs.
Innovation Solution
A method to determine a Quality Rating Data (QRD) for MDAS by categorizing utilization errors and assigning coefficients based on their severity, using a formula that incorporates the total number of utilizations and error categories, allowing for real-time evaluation and action recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time-based failure metrics (MTBF, MTBC) are used to evaluate MDAS reliability, then operational limits can be determined, but the system cannot assess whether the system can survive or exceed these limits in performing capacity
Solution Approach 1:
The patent transforms the reliability assessment from time-based parameters (MTBF, MTBC) to utilization-based parameters (Quality Rating Data). This parameter change allows the system to evaluate reliability in terms of actual clinical exam performance and utilization errors rather than temporal metrics, directly resolving the limitation of not being able to assess system survival capability beyond time-based limits
2Measurement precision
If utilization errors are categorized and coefficients are assigned for QRD calculation, then accurate reliability metric is obtained, but system complexity increases
Solution Approach 1:
The patent segments utilization errors into distinct categories (L1-L4) with assigned coefficients, creating a structured framework for accurate reliability assessment. This segmentation approach balances measurement precision by systematically classifying different error types while maintaining manageable system complexity through standardized categorization rules
Solution Approach 2:
Different coefficient values are assigned to different error categories based on their severity and impact (L1: 0.5, L2: 0.35, L3: 0.01, L4: 0.006). This local quality approach ensures that each error type is weighted appropriately in the QRD calculation, achieving accurate reliability measurement without requiring uniform treatment of all errors
Data Source
AI summary
A method for determining a quality rating data (QRD) of a medical data acquisition system (MDAS) includes receiving first data which includes utilization errors by an MDAS that occurred while performing a data acquisition procedure (DAP), by a data acquisition component of the MDAS, to acquire medical data. The method also includes generating second data as a function of the first data, the second data being indicative of categories of the utilization errors, wherein at least one utilization error is assigned to a corresponding category based on a predetermined parameter. The method further includes determining the quality rating data as a function of a total number of MDAS utilizations, a corresponding coefficient for the corresponding utilization error category and a number of utilization errors in the corresponding error category.