Applying the DMAIC Approach to Reduce Pre-Analytical Errors: Evidence froma Moroccan Medical Laboratory Case Study

Background: Pre-analytical errors are the most common mistakes in medical laboratories and can directly affect patient safety and the accuracy of test results. Typical problems include hemolysis, mislabeling, and issues with sample transport. In Morocco, few studies have looked closely at these errors or tested structured improvement approaches such as DMAIC (Define, Measure, Analyze, Improve, Control).Aim: This study aimed to see whether the DMAIC method could reduce pre-analytical errors in a private laboratory in Casablanca, and whether it could provide practical guidance for similar low-resource laboratories.Methods: We conducted a quasi-experimental study with 324 patients—162 before (September 2023) and 162 after (February 2024) the intervention. Data came from the Laboratory Information System, internal audits, and incident reports. Key indicators included hemolysis, labelingmistakes, and transport problems. We used Pearson’s Chi-square test to compare groups, with p ≤ 0.05 considered significant.Results: The percentage of patients with at least one pre-analytical error dropped from 43.8% (71/162) to 17.9% (29/162) (χ² = 24.67; df = 1; p <0.001). Labeling mistakes fell from 29 to 8 cases, transport issues from 24 to 11, and hemolyzed samples from 12 to 3. The sigma level rose from3.03 to 3.86, showing clear improvement in process reliability. The improvements remained stable over time.Conclusion: Applying the DMAIC method significantly reduced pre-analytical errors and improved laboratory processes. This structured, step-by-step approach can help similar laboratories improve patient safety and test reliability, even with limited resources.