Patient-Based Analyzer QC Using Average of Deltas
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Solution Overview
Problem
Clinical laboratory analyzers often fail to detect intermittent errors due to infrequent quality control analyses, leading to undetected defects and increased costs from unnecessary testing and resource consumption.
Innovation Solution
Implementing a patient-based quality control method using the average of deltas (AoD) and standard deviation of duplicates (SDD) to monitor laboratory analyzers, which calculates differences in analyte measurements over time to detect systematic and random errors, thereby determining the need for recalibration or further investigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional quality control analyses are performed infrequently, then operational costs are reduced, but error detection capability deteriorates
Solution Approach 1:
The system uses patient test results themselves to monitor analyzer performance, eliminating the need for separate control materials and frequent dedicated QC runs. The patient data serves dual purposes: clinical diagnosis and instrument quality control
Solution Approach 2:
Patient test results serve multiple functions simultaneously: providing clinical information for patient care and serving as quality control data for monitoring analyzer performance. This multi-use approach reduces waste of resources
2Reliability
If quality control frequency is increased to detect intermittent errors, then error detection capability is improved, but resource consumption increases
Solution Approach 1:
The system uses existing patient test results to monitor analyzer performance, eliminating the need for separate control materials and frequent dedicated QC runs. The patient data serves dual purposes: clinical diagnosis and instrument quality control
Solution Approach 2:
Patient test results serve multiple functions simultaneously: providing clinical information for patient care and serving as quality control data for monitoring analyzer performance. This multi-use approach reduces waste of resources
3Productivity
If patient-based quality control methods are implemented, then traditional quality control frequency can be reduced, but system complexity increases
Solution Approach 1:
The patent replaces the mechanical/physical system of control materials and manual QC procedures with an information-based system using patient test results and automated statistical analysis. Data processing algorithms replace physical QC workflows
Solution Approach 2:
The system introduces an intermediary computational layer that automatically analyzes patient results to detect analyzer errors. This intermediary processing layer translates raw patient data into quality control decisions without requiring direct human intervention in QC activities
Data Source
AI summary
Generally discussed herein are systems, apparatuses, and methods that relate to detecting an error in a laboratory analyzer. A method can include determining a time delta between consecutive measurements of an analyte made on a patient using a laboratory analyzer, determining whether the time delta is within a specified number of days window and a specified time of day window, determining a measurement value delta between the first and second measurements of the consecutive measurements if the time delta is within the specified number of days and time of day windows, calculating an average of deltas, the average of deltas including a measurement value delta between the consecutive measurements, determining whether the average of deltas is within a specified range of acceptable average of delta values, and issuing an alert if the average of deltas is not within the specified range of acceptable average of delta values.