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Establishment and evaluation of Voting algorithm-based internal quality control (ViQC), a patient-based real-time
Yuan Liu1, Hexiang Zheng2, Wanying Zhang1
1Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China.
Summary
An AI-powered Voting algorithm based internal quality control (ViQC) system enhances patient-based real-time quality control (PBRTQC) by improving error detection efficiency. This advanced method significantly reduces the number of patient samples needed for error identification in clinical settings.
Area of Science:
- Clinical Chemistry
- Artificial Intelligence in Healthcare
- Quality Control Systems
Background:
- Patient-Based Real-Time Quality Control (PBRTQC) is a supplementary program to traditional internal quality control (iQC).
- Existing PBRTQC systems face practical challenges in clinical settings, necessitating improved precision and reliability.
- Artificial Intelligence (AI) offers potential solutions for enhancing quality control mechanisms in healthcare.
Purpose of the Study:
- To introduce and develop an AI-based method, Voting algorithm based iQC (ViQC), for PBRTQC.
- To enhance the precision and reliability of existing PBRTQC systems using AI.
- To evaluate the analytical performance and clinical efficacy of the ViQC algorithm.
Main Methods:
- Retrospective analysis of 111,925 inpatient serum glucose test results from Nanjing Drum Tower Hospital.
- Development of the Voting iQC (ViQC) algorithm based on Voting algorithm principles.
- Evaluation of analytical performance via random error (RE) calculation and comparison with five statistical algorithms (MA, EWMA, MM, MQ, MovSD).
Main Results:
- The ViQC model, incorporating various machine learning models, demonstrated exceptional precision with a false positive rate below 0.002.
- ViQC achieved high accuracy (0.965) at an error factor of 2.0 and an AUC exceeding 0.97 for all evaluated error factors.
- Compared to conventional PBRTQC methods, ViQC significantly improved error detection efficiency, reducing required patient samples from 724 to 168.
Conclusions:
- The AI-based PBRTQC system (ViQC) demonstrated satisfactory performance in a real-world setting.
- ViQC offers superior error detection capabilities compared to traditional PBRTQC statistical methods.
- The developed ViQC algorithm enhances the reliability and efficiency of quality control in clinical laboratory testing.
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