To investigate the application of the exponentially weighted moving average (EWMA) method in the quality control of serum electrolyte assays (K⁺, Na+, Cl^-).

Laboratory test results and intra-laboratory quality control (IQC) data for K⁺, Na⁺, Cl^- from January 2024 to July 2025 were extracted from the hospital laboratory information system (LIS). Patient datasets within an approximately normal distribution range were selected. A machine learning model was developed using Python to perform normality testing by Shapiro-Wilk test, data normalization by Box-Cox transformation with the parameter selection principle of P>0.05 after transformation, parameter optimization, performance validation, and data visualization. The EWMA estimates for patient results were calculated at predefined intervals during the study period, and cumulative coefficients of variation (CV) were computed and compared with the CV targets of IQC. Data visualization was conducted via Z-score quality control charts to identify alerts and analyze potential causes of deviations.

The truncated concentration ranges for patient test results were as follows: K⁺ 2.5–6.0 mmol/L, Na⁺ 125–150 mmol/L, and Cl^- 90–120 mmol/L. The optimal weighting coefficients for K⁺, Na⁺ and Cl^- were all determined to be 0.1,with the optimal step size of 50 for K⁺ and Na⁺, and 60 for Cl^-. During the study period, the cumulative CVs of EWMA for K⁺, Na⁺ and Cl^- were consistently lower than the CV targets and cumulative CVs of IQC. A total of 9 alerts were triggered on the Z-score chart, including 8 true alerts (true positive rate 88.89%) and 1 false alert (false positive rate 11.11%); specifically, K⁺, Na⁺ and Cl^- generated 2, 4, and 3 alerts, respectively. The EWMA model achieved a 100% (8/8) detection rate for systematic errors and provided early warnings before IQC out-of-control for 7 times, while the traditional IQC only had a 12.5% (1/8) detection rate for systematic errors and a 100% (11/11) detection rate for random errors.

The EWMA-based machine learning model, utilizing patient data, is suitable for laboratories with limited sample sizes and complements the limitations of conventional QC methods. This model can make up for the limitations of conventional daily quality control, and its combination with IQC enables the visualization of full-time quality control, as well as the early warning of minor variations in analytical performance and various systematic errors for the determinations of serum K⁺, Na⁺ and Cl^- assays.