Laboratory QC Rule Generation for Faster Failure Detection
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Solution Overview
Problem
Existing laboratory equipment fails to efficiently and quickly identify failures, leading to unnecessary wait times and costs due to complex Westgard rules that are prone to incorrect implementation and not adapted for multiple diagnostic tests.
Innovation Solution
A computer-implemented method and equipment design that simplifies failure detection by calculating an error detection measure based on a number of QC runs, applying a statistical rule to standardized QC results, and determining failures using Mahalanobis distance or chi-square distributions, enabling fast and automated identification of out-of-control conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Westgard rules are used to identify failures in laboratory equipment, then failure detection capability is provided, but the number of runs required increases and implementation complexity increases
Solution Approach 1:
The patent transforms the traditional Westgard rule approach by changing the parameters from multiple separate control rules to a single Mahalanobis distance metric that evaluates multiple QC levels simultaneously. This parameter transformation reduces the number of runs required while maintaining failure detection capability, as the Mahalanobis distance can detect out-of-control conditions more efficiently across multiple dimensions (QC levels and tests) in fewer samples.
Solution Approach 2:
The patent creates a universal failure detection rule that works across multiple diagnostic tests and QC levels simultaneously, rather than requiring separate Westgard rules for each test level combination. The Mahalanobis distance metric serves as a universal detector that handles multiple tests and QC levels in a unified framework, reducing implementation complexity and the number of runs needed.
2Reliability
If Westgard rules are used to identify failures in laboratory equipment, then failure detection capability is provided, but device complexity and training requirements increase
Solution Approach 1:
The patent extracts the essential failure detection function from the complex set of Westgard rules and concentrates it into a single Mahalanobis distance calculation. By taking out the core detection capability and removing the surrounding complexity of multiple rule combinations, the system achieves equivalent or superior reliability with much simpler implementation and automation potential.
Solution Approach 2:
The patent merges multiple separate Westgard rules applied to different QC levels and tests into a single unified Mahalanobis distance metric. This consolidation combines the detection logic for multiple parameters into one computational operation, dramatically reducing device complexity and eliminating the need for complex rule selection and training while maintaining comprehensive monitoring.
3Reliability
If traditional QC monitoring with multiple runs is used, then statistical reliability is improved, but productivity decreases due to longer wait times
Solution Approach 1:
The patent performs preliminary statistical setup by pre-calculating the mean and covariance matrix from historical in-control data, storing these parameters for rapid Mahalanobis distance calculation during operation. This preliminary action enables fast real-time detection without requiring extensive runs at the time of testing, thus maintaining statistical reliability while improving productivity and reducing patient wait times.
Data Source
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AI summary
Given a computer-implemented method for determining failures in laboratory equipment it is an objective of the present invention to simplify the fast detection of failure in laboratory test equipment. The objective is solved by the method comprising the rule generation steps: a) receiving a desired probability of false rejection (P̌ fr) and a desired error detection rate ĚD, such as a desired probability of error detection (P̌ed), relating to one or more QC levels (J) of quality control (QC) samples to be processed by the equipment; b) setting a number of runs (R) to one; c) calculating an error detection rate (ÊD) based at least partially on R; d) determining if ÊD is below ĚD, and if so: increase R by one, and repeat steps c) to d), and if not: define a rule for determining failures in the laboratory equipment based, at least partially, on R, the method further comprising rule application steps: e) receiving or collecting standardized QC results, preferably from at least R runs of QC samples generated by the equipment; f) applying the rule defined in step d) to the standardized QC results; g) determining failures in the equipment, if the standardized QC results comply with the rule.