Offering a concise yet comprehensive review of current practices in surgery and patient safety, Handbook of Perioperative and Procedural Patient Safety is an up-to date, practical resource for practicing surgeons, anesthesiologists, surgical nurses, hospital administrators, and surgical office staff. Edited by Drs. Juan A. Sanchez and Robert S. D. Higgins and authored by expert contributors from Johns Hopkins, it provides an expansive look at the scope of the problem, causes of error, minimizing errors, surgical suite and surgical team design, patient experience, and other related topics. Presents the knowledge and experience of a multidisciplinary team from Johns Hopkins University, which created the Comprehensive Unit-based Safety Program (CUSP), an approach for improving safety culture and engaging frontline clinicians to identify and mitigate defects in care delivery. Discusses the scope and prevalence of perioperative harm, causes of error in healthcare, and perioperative never events. Covers safe practices, cognitive workload and fatigue, and the effects of noise in the OR. Includes several team-based chapters such as the dynamics of surgical teams, safer perioperative team communication, and the culture of safety. Consolidates today's available information and guidance into a single, convenient resource.

Guest Editor Juan Sanchez reviews articles in Safe Surgery for the general surgeon. Articles include iatrogenesis: the nature, frequency, and science of medical errors, risk management and the regulatory framework for safer surgery medication, lab, and blood banking errors, surgeons' non-technical skills, creating safe and effective surgical teams, human factors and operating room safety, systemic analysis of adverse events: identifying root causes and latent errors, information technologies and patient safety, patient safety and the surgical workforce, measuring and preventing healthcare associated infections, the surgeon's four-phase reaction to error, universal protocols and wrong-site/wrong-patient events, unconscious biases and patient safety, and much more

Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines

Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to inferences about specific populations. The book is divided into two parts. Part I introduces readers to fundamental definitions, theorems, and methods within the context of discrete sample spaces. It addresses the origin of the mathematical study of probability, main concepts in modern probability theory, univariate and bivariate discrete probability models, and the multinomial distribution. Part II builds upon the knowledge imparted in Part I to present students with corresponding ideas in the context of continuous sample spaces. It examines models for single and multiple continuous random variables and the application of probability theorems in statistics. Probability for Data Scientists effectively introduces students to key concepts in probability and demonstrates how a small set of methodologies can be applied to a plethora of contextually unrelated problems. It is well suited for courses in statistics, data science, machine learning theory, or any course with an emphasis in probability. Numerous exercises, some of which provide R software code to conduct experiments that illustrate the laws of probability, are provided in each chapter.