Data-driven Real-time Quality Control: Prospective Implementation of Patient-based Moving Average Algorithms in a Large-size Clinical Biochemistry Laboratory

Introduction:The standard internal quality control (IQC) process of clinical laboratories uses scheduled testing of commercial control materials which leads to missed detection of analytical changes that happen between control tests.The patient-based real-time quality control (PBRTQC) system uses moving average (MA) algorithms as its main method to provide ongoing monitoring of analytical processes.Moving average algorithms were tested in this research study to determine their effectiveness and diagnostic accuracy and practical application for IQC at a high-capacity clinical biochemistry laboratory. Materials and methods:The research team performed a prospective observational study through their method-evaluation research which took place within a tertiary care clinical biochemistry laboratory that handled 12,000 daily tests during their 6 months study period.The researchers assessed high-volume analytes which included sodium, potassium, glucose, urea, creatinine, and thyroid profile components thyroid-stimulating hormone (TSH), free T3 , and free T4.The first phase started with a 2 months retrospective study during which the team optimized MA parameters through three window size tests of 20, 30, and 50 together with specific truncation limits.The second phase used improved MA algorithms to monitor operations in real-time while traditional IQC methods continued to function.The study measured results through sensitivity tests and specificity assessments which recorded the time needed to identify analytical changes and counted the number of false alerts while testing operational viability through Cohen's κ agreement measurement.The researchers conducted statistical assessments by using receiver operating characteristic (ROC) curves together with McNemar's test and paired t-tests and Chi-square testing and κ statistics.Results: Moving average algorithms demonstrated significantly higher sensitivity compared to conventional IQC for most analytes (e.g., sodium: 0.95 vs 0.85, p = 0.012; potassium: 0.92 vs 0.80, p = 0.018).Specificity was also superior (sodium: 0.98 vs 0.92, p = 0.021).For thyroid analytes, MA showed improved sensitivity and specificity, reaching statistical significance for TSH and free T3 (p < 0.05), while free T4 showed borderline significance.Moving average significantly reduced time-to-detection across all analytes (mean reduction 15-22 minutes; p < 0.001), representing a 40-50% improvement compared to IQC.Receiver operating characteristic analysis demonstrated optimal performance with window sizes of 30-50 (AUC: 0.90-0.92).False alert frequency was significantly lower in thyroid assays (p < 0.05).Concordance between MA and IQC was moderate-to-strong (κ = 0.64-0.82;p < 0.001).Operational feedback showed 75% positive acceptance, with no significant workflow disruption or additional consumable cost.Conclusion: Patient-based MA algorithms significantly enhance analytical error detection, reduce time-to-detection, and maintain high specificity compared to conventional IQC.When optimally configured, MA serves as a robust, cost-neutral adjunct to traditional quality control systems.Integration of MA into routine laboratory workflows strengthens real-time analytical surveillance and supports improved patient safety, particularly in high-volume and resource-limited clinical settings.