Statistical Analysis for Decision Makers in Healthcare


Book Description

Americans are bombarded with statistical data each and every day, and healthcare professionals are no exception. All segments of healthcare rely on data provided by insurance companies, consultants, research firms, and the federal government to help them make a host of decisions regarding the delivery of medical services. But while these health pro




Decision Making in Health and Medicine


Book Description

A guide for everyone involved in medical decision making to plot a clear course through complex and conflicting benefits and risks.




Statistical Methods for Evaluating Safety in Medical Product Development


Book Description

This book gives professionals in clinical research valuable information on the challenging issues of the design, execution, and management of clinical trials, and how to resolve these issues effectively. It also provides understanding and practical guidance on the application of contemporary statistical methods to contemporary issues in safety evaluation during medical product development. Each chapter provides sufficient detail to the reader to undertake the design and analysis of experiments at various stages of product development, including comprehensive references to the relevant literature. Provides a guide to statistical methods and application in medical product development Assists readers in undertaking design and analysis of experiments at various stages of product development Features case studies throughout the book, as well as, SAS and R code




Decision Analysis for Healthcare Managers


Book Description

The first part of the book explains the various analytical tools that simplify and accelerate decision making. Learn about tools that help you determine causes, evaluate choices, and forecast future events. For occasions when a group, rather than an individual, has to make a decision, you will also learn what tools can help you create group consensus. The second half of the book shows you how to apply analytical tools to different healthcare situations, including comparing clinician performance, determining the causes for medical errors, analyzing the costs of programs, and determining the market for new services. Many practical examples walk you step-by-step through common decision-making scenarios.




Introduction to Probability and Statistics for Ecosystem Managers


Book Description

Explores computer-intensive probability and statistics for ecosystem management decision making Simulation is an accessible way to explain probability and stochastic model behavior to beginners. This book introduces probability and statistics to future and practicing ecosystem managers by providing a comprehensive treatment of these two areas. The author presents a self-contained introduction for individuals involved in monitoring, assessing, and managing ecosystems and features intuitive, simulation-based explanations of probabilistic and statistical concepts. Mathematical programming details are provided for estimating ecosystem model parameters with Minimum Distance, a robust and computer-intensive method. The majority of examples illustrate how probability and statistics can be applied to ecosystem management challenges. There are over 50 exercises – making this book suitable for a lecture course in a natural resource and/or wildlife management department, or as the main text in a program of self-study. Key features: Reviews different approaches to wildlife and ecosystem management and inference. Uses simulation as an accessible way to explain probability and stochastic model behavior to beginners. Covers material from basic probability through to hierarchical Bayesian models and spatial/ spatio-temporal statistical inference. Provides detailed instructions for using R, along with complete R programs to recreate the output of the many examples presented. Provides an introduction to Geographic Information Systems (GIS) along with examples from Quantum GIS, a free GIS software package. A companion website featuring all R code and data used throughout the book. Solutions to all exercises are presented along with an online intelligent tutoring system that supports readers who are using the book for self-study.




Introduction to Health Informatics, Second Edition


Book Description

The first resource of its kind, Introduction to Health Informatics examined the effects of health informatics on healthcare practitioners, patients, and policies from a distinctly Canadian perspective. This second edition has been thoroughly updated to reflect current trends and innovations in health informatics and includes new figures, charts, tables, and web links. In this text, author Christo El Morr presents the subject of health informatics in an accessible, concise way, breaking the topic down into 12 chapters divided into 3 sections. Each chapter includes objectives, key terms, which are defined in a full glossary at the end of the text, and a “Test Your Understanding” section for student review. The second edition also features 15% brand new content, with a full chapter on analytics, machine learning, and AI for health, as well as information on virtual care, mHealth apps, COVID-19 responses, adoption of EHR across provinces, clinical informatics, and precisions medicine. Packed with pedagogical features and updated instructor supplements, this text is a vital resource for students, instructors, and practitioners in health informatics, health management, and health policy. FEATURES: - Takes a uniquely Canadian perspective on health informatics - Contains 15 percent new content on topics such as virtual care, mHealth apps, COVID-19 responses, adoption of EHR across provinces, clinical informatics, and precisions medicine - Updated instructor supplements, including PowerPoint slides and a test bank




Business Analytics and Statistics, 2nd Edition


Book Description

Written for the Australian and New Zealand markets, the second edition of Business Analytics & Statistics (Black et al.) presents statistics in a cutting-edge interactive digital format designed to motivate students by taking the road blocks out of self-study and to facilitate master through drill-and-skill practice.




Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science


Book Description

Bayesian Networks “This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation.” Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates diffculties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader’s own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.




Operations Management in Healthcare, Second Edition


Book Description

This thoroughly revised and updated second edition of Operations Management in Healthcare: Strategy and Practice describes how healthcare organizations can cultivate a competitive lead by developing superior operations using a strategic perspective. In clearly demonstrating the "how-tos" of effectively managing a healthcare organization, this new edition also addresses the "why" of providing quality and value-based care. Comprehensive and practice-oriented, chapters illustrate how to excel in the four competitive priorities - quality, cost, delivery, and flexibility - in order to build a cumulative model of healthcare operations in which all concepts and tools fit together. This textbook encourages a hands-on approach and integrates mind maps to connect concepts, icons for quick reference, dashboards for measurement and tracking of progress, and newly updated end-of-chapter problems and assignments to reinforce creative and critical thinking. Written with the diverse learning needs in mind for programs in health administration, public health, business administration, public administration, and nursing, the textbook equips students with essential high-level problem-solving and process improvement skills. The book reveals concepts and tools through a series of short vignettes of a fictitious healthcare organization as it embarks on its journey to becoming a highly reliable organization. This second edition also includes a strong emphasis on the patient's perspective as well as expanded and added coverage of Lean Six Sigma, value-based payment models, vertical integration, mergers and acquisitions, artificial intelligence, population health, and more to reflect evolving innovations in the healthcare environment across the United States. Complete with a full and updated suite of Instructor Resources, including Instructor’s Manual, PowerPoints, and test bank in addition to data sets, tutorial videos, and Excel templates for students. Key Features: Demonstrates the "how-tos" of effectively managing a healthcare organization Sharpens problem-solving and process improvement skills through use of an extensive toolkit developed throughout the text Prepares students for Lean Six Sigma certification with expanded coverage of concepts, tools, and analytics Highlights new trends in healthcare management with coverage of value-based payments, mergers and acquisitions, population health, telehealth, and more Intertwines concepts with vivid vignettes to describe human dynamics, organizational challenges, and applications of tools Employs boxed features and YouTube videos to address frequently asked questions and real-world instances of operations in practice




Bayesian Biostatistics


Book Description

The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics illustrated via a diverse range of applications taken from epidemiology, exploratory clinical studies, health promotion studies, image analysis and clinical trials. Key Features: Provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementation, with an emphasis on healthcare techniques. Contains introductory explanations of Bayesian principles common to all areas of application. Presents clear and concise examples in biostatistics applications such as clinical trials, longitudinal studies, bioassay, survival, image analysis and bioinformatics. Illustrated throughout with examples using software including WinBUGS, OpenBUGS, SAS and various dedicated R programs. Highlights the differences between the Bayesian and classical approaches. Supported by an accompanying website hosting free software and case study guides. Bayesian Biostatistics introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful.