Automated Sample Collection Error Detection in Clinical Assays
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Solution Overview
Problem
Current blood analysis systems fail to accurately distinguish and alert users to incorrect sample collection methods, particularly due to the use of inappropriate anticoagulants, leading to potentially erroneous test results that can impact patient health.
Innovation Solution
An algorithmic method within the analysis system that identifies the presence of incorrect anticoagulants by analyzing the concentration of specific ions and analytes, flagging affected tests and providing alerts to ensure accurate sample collection, using sensors like ion-selective electrodes to determine divalent and monovalent cations and other analytes, and setting threshold values to report inappropriate samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select collection tubes based on color-coded stoppers, then sample collection can be performed, but errors in tube selection occur due to user distraction and workload
Solution Approach 1:
The system provides automated feedback by analyzing test results and comparing them against expected ranges. When results indicate incorrect anticoagulant usage, the system flags the error and alerts the user, creating a closed-loop feedback mechanism that corrects selection errors after they occur.
Solution Approach 2:
The system performs self-verification by automatically detecting incorrect tube selection through algorithmic analysis of test results, eliminating the need for manual verification by the user. The analyzer independently identifies and reports errors without requiring additional user attention.
2Reliability
If reportable ranges are used to flag abnormal results, then some errors can be detected, but the system cannot distinguish between sensor malfunctions and incorrect sample collection
Solution Approach 1:
The system segments the error detection process by analyzing multiple individual test results separately and comparing each against expected physiological ranges. This granular analysis allows the system to identify patterns specific to incorrect anticoagulant usage rather than treating all abnormal results as a single category.
Solution Approach 2:
The system changes the detection parameter from simple reportable range flagging to multi-parameter algorithmic analysis. By evaluating multiple analytes simultaneously and comparing their interrelationships against expected physiological correlations, the system can distinguish between sensor malfunctions and collection errors.
3Adaptability or versatility
If multiple anticoagulant types are available for different tests, then test versatility is improved, but the complexity of selecting the correct tube increases
Solution Approach 1:
The system provides a universal solution that works across all anticoagulant types and test configurations. The algorithm automatically adapts to different tube types by analyzing the specific pattern of affected analytes, eliminating the need for users to memorize which tube to use for each test.
Solution Approach 2:
The system introduces an intermediary layer (the algorithmic analysis system) between the diverse tube types and the user. This intermediary automatically interprets the complex relationships between different anticoagulants and test requirements, presenting a simplified interface to the user.
4Reliability
If prior art systems flag out-of-range results, then abnormal values are identified, but no explicit alert is provided that the wrong collection device was used
Solution Approach 1:
The system provides explicit feedback by not only flagging abnormal results but also communicating the specific cause (incorrect collection device) to the user through clear alerts and recommendations for corrective action.
Solution Approach 2:
The system introduces an intermediary communication layer that translates complex analytical findings into user-friendly alerts. The intermediary converts algorithmic error detection into actionable information that clearly guides the user on what went wrong and how to fix it.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively prevents and detects erroneous sample collections by flagging affected results and alerting users to the correct anticoagulant usage, ensuring clinically valid results and improving the reliability of point-of-care testing.
Implementation Method 1
using sensors like ion-selective electrodes to determine divalent and monovalent cations and other analytes
Data Source
AI summary
An automatic method for identifying biological samples that are collected using the wrong blood preservative for subsequent analytical testing. The method also provides for identification and/or suppression of certain analytical test results that are substantially or partly adversely affected. The invention is particularly suited for use in point-of-care medical diagnostic testing.