Unified Sigma-Metric Computation for Clinical Diagnostics
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Solution Overview
Problem
Current methods for determining the sigma-metric in clinical diagnostic processes assume homoscedasticity, which is rarely true, leading to multiple sigmas for a single process and uncertainty about which sigma is correct, making it difficult to accurately assess error tolerance and quality control.
Innovation Solution
A computer-implemented method and system that acquire specimen data from laboratory instruments, determine analytical standard deviations, assign standard deviations to patient analyte values based on corresponding concentrations, and compute a single sigma-metric by calculating the ratio of Total Allowable Error to standard deviation, providing a unified sigma value for the clinical diagnostic process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine sigma-metrics in clinical processes, then multiple sigmas are obtained for different concentration levels, but it becomes unclear which sigma is correct and how to use the sigma-metric
Solution Approach 1:
The patent transforms the sigma-metric calculation from concentration-specific parameters to a unified metric by changing the reference frame from individual control level sigmas to a patient-level sigma that aggregates across all concentrations. This parameter transformation resolves the ambiguity by creating a single sigma value that represents overall process performance rather than isolated concentration points.
Solution Approach 2:
The invention creates a universal sigma-metric that serves multiple functions simultaneously: it evaluates process performance across all concentration levels, provides a single comparable value for different clinical processes, and maintains relevance to patient outcomes. This multi-functional metric eliminates the need to choose between multiple concentration-specific sigmas.
2Reliability
If separate sigmas are displayed for each level of control material, then all concentration variations are captured, but the complexity of interpretation increases and a single process quality metric is not achieved
Solution Approach 1:
The patent merges multiple concentration-specific sigma values into a single unified sigma-metric by combining patient data across all control levels. This consolidation maintains the reliability of quality control by incorporating all concentration variations while simplifying interpretation to a single process quality metric that reflects overall performance.
3Ease of manufacture
If homoscedasticity is assumed in sigma calculation, then the calculation is simplified, but the accuracy decreases because clinical processes are rarely homoscedastic
Solution Approach 1:
The invention introduces dynamics into the sigma-metric calculation by using patient-level data that naturally captures variation across different concentrations. Rather than assuming static homoscedasticity, the method dynamically adapts to the actual heteroscedastic nature of clinical processes through empirical patient data, achieving both accuracy and computational feasibility.
Data Source
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AI summary
A system and method for determining a sigma of a clinical diagnostic process are disclosed. Specimen data are collected from a plurality of laboratory instruments. The specimen data are evaluated to determine a concentration and an analytical standard deviation for each data point. A clinical diagnostic process is run and patient analyte values are acquired, and a standard deviation is assigned to each patient analyte value based on the standard deviation of specimen data having a corresponding concentration. A single sigma-metric is computed based on the patient analyte assigned standard deviations, the sigma- metric representing the sigma of the clinical diagnostic process. The computed sigma-metric is reported to a user or laboratory manager.