Statistical process control within the Six Sigma DMAIC framework for monitoring medication dispensing errors in hospital pharmacies: a Saudi case study.

This study employed the Six Sigma DMAIC methodology to monitor and analyze medication dispensing errors in hospital pharmacies in Saudi Arabia, with a focus on Mecca and Jeddah cities. Using aggregated data from the Ministry of Health (MOH) Statistical Yearbook 2024, a synthetic dataset was generated through a Conditional Tabular Generative Adversarial Network (CTGAN) to enable the Statistical Process Control (SPC) analysis. The p-chart application demonstrated how process stability can be evaluated within a synthetic healthcare dataset, while the calculated sigma level (2.49) represents the performance characteristics of the generated data rather than actual hospital pharmacy performance. Pareto analysis illustrated how dominant error categories can be prioritized within a synthetic dataset generated under predefined assumptions. Based on these findings, several improvement strategies were proposed, particularly targeting dispensing errors within pharmacy operations. However, due to the use of synthetic data, the improvement and control phases remain conceptual. The generated synthetic dataset enabled demonstration of Statistical Process Control (SPC) techniques within the Six Sigma DMAIC framework. The p-chart indicated statistical stability within the generated data, while Pareto analysis illustrated the prioritization of error categories under predefined synthetic distributions. Because all analyses were conducted using CTGAN-generated synthetic records, the findings should be interpreted as a methodological demonstration rather than as evidence of actual medication error performance in Saudi hospital pharmacies.