This book provides a detailed description of how to apply Lean Six Sigma in the health care industry, with a special emphasis on process improvement and operations management in hospitals. This book begins with a description of the Enterprise Performance Excellence (EPE) improvement methodology developed by the author that links several methodologies including systems thinking, theory of constraints, Lean and Six Sigma to provide an enterprise-wide prioritization and value-chain view of health care. The EPE methodology helps to improve flow at the macro or value-chain level, and then identifies Lean Six Sigma detailed improvements that can further improve processes within the value-chain. This book also provides real-world health care applications of the EPE and Lean Six Sigma methodologies that showed significant results on throughput, capacity, operational, and financial performance. The Enterprise Performance Excellence methodology is described, and also the Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) problem-solving approach which is used to solve problems for health care processes as they are applied to real-world cases. The case studies include a wide variety of processes and problems including emergency department throughput improvement; operating room turnaround; operating room organization; leaning the surgical outpatient screening process; CT imaging diagnostic test reduction in an emergency department; reducing light exposure for infants in a NICU; implementing sepsis protocols in an emergency department; improving timeliness and satisfaction in interventional radiology processes; incorporating methods for defining labor standards in a hospital; proposing a framework for patient-centered care; and critical success factors of an enterprise performance excellence program. The second edition of Lean Six Sigma for the Healthcare Enterprise includes new and additional case studies. The case studies now incorporate more systems engineering modeling, simulation modeling, applying Artificial Intelligence (AI) techniques to identify inefficiencies and opportunities for improvement, as well as further advanced data and statistical analysis.