Background: Internal quality control (IQC) data offers continuous insight into analytical performance under routine conditions. This study evaluated IQC practices and long-term analytical imprecision (CV_a) across primary healthcare laboratories to derive analyte-specific reference change values (RCVs) for non-communicable disease (NCD) monitoring. Methods: A 22-month retrospective analysis of IQC data was conducted across 29 primary healthcare laboratories using 32 analytical units (Beckman Coulter AU480) in Malaysian primary healthcare. Six analytes were assessed: glucose, creatinine, total cholesterol, triglycerides, HDL cholesterol, and ALT. CV_a was estimated using median and 90th percentile (P90) coefficients of variation across two concentration levels. RCVs were calculated at 95% probability (Z = 1.96) by integrating observed CV_a with within-subject biological variation (CV_i) from EFLM databases. Results: IQC testing was highly standardized (median: 20 measurements/month). Long-term data showed stable, concentration-dependent imprecision. Median CV_a was lowest for glucose and lipids (1.7–1.9%) but higher for ALT (3.79%) and creatinine (3.52%) at Level 1. Derived RCV ranged from 14% (glucose) to 55.1% (triglycerides), with CV_i being the dominant contributor to RCV magnitude for most analytes. Conclusions: Long-term routine IQC data provide an analytically realistic foundation for deriving RCV in primary healthcare by reflecting real-world performance. Applying these RCV supports evidence-based interpretation of serial results, enhancing NCD monitoring by distinguishing true physiological change from analytical and biological noise.