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Six sigma is a structured methodology that uses mathematical and statistical tools to increase productivity and business efficiency. In order to apply it, it is necessary to have a very broad knowledge in the fields of mathematics, management, and sector to which the process or the product under consideration (mechanical, electronic, chemical, etc.) refers. It is based on guidelines with a rigid and formalized approach to deal with misunderstandings and minimize subjectivity as much as possible.

This chapter will provide the basic information regarding the birth of the methodology and the composition of the work team; moreover, the procedure define, measure, analyze, improve, and control (DMAIC) will be presented and some tools will be described in order to obtain the improvements desired for the product or the process.

Although it is widely acknowledged that health care delivery systems are complex adaptive systems, there are gaps in understanding the application of systems engineering approaches to systems analysis and redesign in the health care domain. Commonly employed methods, such as statistical analysis of risk factors and outcomes, are simply not adequate to robustly characterize all system requirements and facilitate reliable design of complex care delivery systems. This is especially apparent in institutional-level systems, such as patient safety programs that must mitigate the risk of infections and other complications that can occur in virtually any setting providing direct and indirect patient care. The case example presented here illustrates the application of various system engineering methods to identify requirements and intervention candidates for a critical patient safety problem known as failure to rescue. Detailed descriptions of the analysis methods and their application are presented along with specific analysis artifacts related to the failure to rescue case study. Given the prevalence of complex systems in health care, this practical and effective approach provides an important example of how systems engineering methods can effectively address the shortcomings in current health care analysis and design, where complex systems are increasingly prevalent.