Clinical Analyzer Short Sample Detection via Statistical Probability
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Solution Overview
Problem
Current methods for detecting short and long samples in clinical analyzers are unreliable and subjective, leading to inaccurate test results due to human and machine errors, which can have devastating consequences in diagnosis and monitoring.
Innovation Solution
A system comprising a data module, evaluation module, and optional analyzer module that uses reference data and statistical analysis to determine the probability of a sample being a short, long, or acceptable sample by analyzing measurable characteristics and their distributions, thereby identifying potential errors in sample concentration or volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physiological-based detection methods are used to identify short samples, then diagnostic reliability can be improved, but the method becomes subjective and imprecise
Solution Approach 1:
The patent replaces subjective physiological-based detection methods with an automated computational system that uses statistical analysis and curve fitting algorithms. The system automatically compares measured analyte concentrations against expected physiological ranges and dilution patterns, eliminating human subjectivity while maintaining detection reliability.
Solution Approach 2:
The system enables self-detection by automatically analyzing the relationship between multiple analyte measurements and dilution factors. The computational algorithm independently identifies short samples by detecting inconsistencies in the expected physiological relationships between analytes, without requiring external physiological reference data or expert interpretation.
2Device complexity
If no detection system is used, then device complexity is reduced, but measurement precision deteriorates due to undetected short and long samples
Solution Approach 1:
The patent introduces a computational intermediary layer that sits between the clinical analyzer and the final results. This software module acts as a mediator that automatically processes raw analyte measurements, applies statistical analysis, and flags potential short or long samples before results are reported, adding minimal complexity while significantly improving measurement precision.
Solution Approach 2:
The system implements feedback by continuously monitoring the relationships between multiple analyte measurements and comparing them against expected physiological patterns. When inconsistencies are detected that suggest short or long samples, the system provides feedback signals to alert operators and potentially trigger retesting, creating a self-correcting measurement system.
Data Source
AI summary
Methods and systems are provided for detecting the accidental use of short and long samples in the clinical analysis of a sample, specimen, or assay. The systems can include a clinical analyzer for determining one or more values for one or more measurable characteristics of a sample. These values are used in combination with reference data stored in a data module to generate a probability that the sample tested is a short sample, a long sample, or an acceptable sample. This probability and/or the status of the sample as a short sample, a long sample, or an acceptable sample are output to a user.