Automated Sample Tube Set Identification in Laboratory Automation
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Solution Overview
Problem
Establishing a sample tube set for laboratory automation systems is inefficient, time-consuming, and error-prone, particularly when new or different tube types need to be integrated, as technicians spend significant time configuring detection parameters and resolving conflicts, often requiring higher support levels.
Innovation Solution
A method that selects a sample tube set by obtaining parameter distributions for detection parameters, determining if the laboratory automation system can correctly identify each tube type, and proposing conflict remediations to ensure the system can process the tubes, including options to remove or replace conflicting tube types or adjust parameters for accurate identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If technicians manually configure detection parameters and resolve conflicts for new sample tube types, then the laboratory automation system can correctly identify and process the tubes, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by automatically obtaining parameter distributions for detection parameters of sample tube types and comparing these distributions to determine identification capability before actual processing begins. This pre-configuration approach eliminates manual technician intervention and reduces setup time while ensuring reliable tube identification.
Solution Approach 2:
The laboratory automation system serves itself by automatically determining whether it can correctly identify each sample tube type through parameter distribution comparison, and by autonomously proposing conflict remediations. This self-service capability removes the need for manual technician configuration and conflict resolution, significantly reducing setup time while maintaining identification reliability.
2Adaptability or versatility
If multiple different sample tube types are integrated into the system, then the system becomes more versatile, but the complexity of configuring and managing the tube set increases
Solution Approach 1:
The system manages complexity by focusing on parameter distributions (mean, standard deviation, skewness, kurtosis) of detection parameters across different sample tube types. By comparing these statistical parameters rather than manually configuring each tube type, the system achieves high versatility in handling multiple tube types while keeping the configuration process simple and automated.
Solution Approach 2:
The parameter distribution comparison approach serves multiple functions: it determines identification capability, detects conflicts between tube types, and proposes remediations. This universal method handles diverse sample tube types without requiring separate configuration procedures for each tube type, thereby increasing versatility while managing complexity.
3Reliability
If technicians perform on-site teaching and testing for each instrument, then the sample tube configuration is validated, but the process becomes error-prone and requires higher support levels
Solution Approach 1:
The system obtains parameter distributions from previously detected reference data values, creating a feedback mechanism that uses historical detection data to validate and refine tube type identification. This automated feedback loop replaces manual on-site teaching and testing, ensuring configuration validation while eliminating human errors and reducing the need for higher support levels.
Data Source
AI summary
A method of establishing a sample tube set which is adapted to be processed by a laboratory automation system. The method includes selecting a sample tube set comprising several sample tube types by selecting a plurality of different sample tube types from an assortment of available sample tube types; obtaining a parameter of distribution for at least one detection parameter of each sample tube type comprised in said sample tube set, wherein the parameter of distribution comprises information regarding a distribution of previously detected reference data values of the at least one detection parameter; determining whether the laboratory automation system is capable of correctly identifying each sample tube type comprised in said sample tube set by comparing the parameter of distribution for the at least one detection parameter of each sample tube type comprised in said sample tube set with the parameter of distribution for the at least one detection parameter of all the other sample tube types comprised in said sample tube set; and indicating that the selected sample tube set is approved for being processed by the laboratory automation system if it is determined that the laboratory automation system is capable of correctly identifying each sample tube type comprised in said sample tube set, or proposing at least one conflict remediation if it is determined that the laboratory automation system is not capable of correctly identifying each sample tube type comprised in said sample tube set.