Laboratory Analyzer Error Detection via Patient Data Deltas
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Solution Overview
Problem
Current laboratory analyzers often fail to detect intermittent errors due to infrequent quality control analyses, leading to undetected defects in test results, which can be costly and time-consuming to address, and existing patient data analysis techniques struggle to differentiate between analytical shifts and random errors.
Innovation Solution
Implementing a system that calculates the Average of Deltas (AoD) for repeated patient measurements within a 16-32 hour window, using truncation limits to filter out unstable data and determine if the laboratory analyzer requires recalibration by comparing AoD values to predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quality control analyses are performed frequently to detect intermittent errors, then error detection capability is improved, but productivity deteriorates due to increased time consumption and delays in patient result reporting
Solution Approach 1:
The system performs preliminary error detection by continuously monitoring patient measurement data and calculating deltas between repeated measurements. This preliminary detection mechanism identifies potential analyzer errors before they affect quality control results, allowing for proactive calibration scheduling that prevents errors from going undetected while maintaining normal productivity levels.
Solution Approach 2:
The patent introduces an intermediary error detection mechanism that operates between routine patient testing and formal quality control analyses. By monitoring patient data for patterns indicating analyzer drift or errors, the system provides continuous error detection without requiring frequent interruption for quality control samples, thus maintaining productivity while improving reliability.
2Productivity
If quality control analyses are performed infrequently to maintain productivity, then patient result reporting speed is improved, but error detection capability deteriorates leading to undetected defects
Solution Approach 1:
The system maintains continuous error detection capability by constantly monitoring patient measurement data and calculating deltas between repeated measurements. This continuous monitoring operates in the background without interrupting the main patient testing workflow, ensuring that errors are detected promptly while productivity remains high and patient results are reported without delay.
3Reliability
If patient data analysis is used to detect analytical shifts, then error detection is improved, but the ability to differentiate between analytical shifts and random errors deteriorates
Solution Approach 1:
The patent applies local quality by treating different patient populations and measurement contexts differently in the analysis. By identifying and weighting measurements based on their reliability and contextual factors, the system can better distinguish between true analytical shifts and random variations, improving differentiation accuracy while maintaining error detection capability.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as delta thresholds and weighting factors based on the specific analyte, patient population, and measurement conditions. These parameter changes allow the system to optimize its ability to differentiate between analytical shifts and random errors for different clinical contexts, improving measurement precision in error differentiation.
Data Source
AI summary
Generally discussed herein are systems, apparatuses, and methods that relate to detecting an error in a laboratory analyzer. A device may include an average of deltas (AoD) module to receive pairs of consecutive measurement values of an analyte of one or more patients, each pair of consecutive measurement values including a first measurement of an analyte obtained from a patient of the one or more patient at a first time and a second measurement of an analyte obtained from the patient at a second time after the first time, determine a time delta between each pair of consecutive measurement values, determine whether the time delta is within a specified time window, determine a measurement value deltas between each pair of consecutive measurement values that includes a time delta with the specified time window, and determine an AoD using the determined measurement value deltas.