Factorial experimental design and six SIGMA as tools for process improvement in healthcare

This paper explores the application of Six SIGMA methodology and Design of Experiments (DoE) for improving healthcare processes, using a case study based on data from the Protecal polyclinic. The objective is to demonstrate how statistical and data-driven approaches can enhance process efficiency, reduce variability, and optimize patient outcomes. The research follows the DMAIC (Define, Measure, Analyze, Improve, Control) framework to structure the improvement process. Within this framework, a full factorial experimental design (2³) is applied to analyze the effects of three key health indicators: ALT, total cholesterol, and glucose on patient body weight. Eight experimental combinations with replications are used to evaluate both main and interaction effects. Regression analysis is employed to model the relationship between input variables and the response, enabling identification of the most significant factors influencing outcomes. The results show that the combined use of Six SIGMA and DoE provides an efficient and systematic approach to healthcare optimization, minimizing experimental effort while maximizing information. The study confirms the potential of integrating industrial engineering methods into healthcare systems to support evidence-based decision making and improve overall quality of care.