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This book examines the nature of medical knowledge, how it is obtained, and how it can be used for decision support. It provides complete coverage of computational approaches to clinical decision-making. Chapters discuss data integration into healthcare information systems and delivery to point of care for providers, as well as facilitation of direct to consumer access. A case study section highlights critical lessons learned, while another portion of the work examines biostatistical methods including data mining, predictive modelling, and analysis. This book additionally addresses organizational, technical, and business challenges in order to successfully implement a computer-aided decision-making support system in healthcare delivery.

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Medical decision making has evolved in recent years, as more complex problems are being faced and addressed based on increasingly large amounts of data. In parallel, advances in computing have led to a host of new and powerful statistical tools to support decision making. Simulation-based Bayesian methods are especially promising, as they provide a unified framework for data collection, inference, and decision making. In addition, these methods are simple to interpret, and can help to address the most pressing practical and ethical concerns arising in medical decision making.
* Provides an overview of the necessary methodological background, including Bayesian inference, Monte Carlo simulation, and utility theory.
* Driven by three real applications, presented as extensively detailed case studies.
* Case studies include simplified versions of the analysis, to approach complex modelling in stages.
* Features coverage of meta-analysis, decision analysis, and comprehensive decision modeling.
* Accessible to readers with only a basic statistical knowledge.
Primarily aimed at students and practitioners of biostatistics, the book will also appeal to those working in statistics, medical informatics, evidence-based medicine, health economics, health services research, and health policy.

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Given with clear, systematic direction, the methods discussed in this book are an important first step in providing a rigorous, standardized approach to assessing the effectiveness and economic impact of public health prevention interventions. Developed from a course given at the Centers for Disease Control and Prevention, the Guide was reviewed by a panel of nationally-recognized experts in public health, decision analysis and economic analysis. It gives how-to explanations of study design, decision analysis, measures of effectiveness, and measures of cost, including cost-benefit analysis, cost-effectiveness analysis, and cost-utility analysis. Appendices contain a variety of useful material including an account of illustrative case studies in decision analysis and economic analysis, numerous data tables and indices, and a discussion of attribution. In addition to providing systematic methodology, the book makes specific recommendations on standardizing methods for discount rates, study perspective, willingness-to-pay measures, analytic horizon and more.
Decision-makers at the national, state and local levels will find this book very helpful in building on traditional areas of biomedical science, epidemiology, and statistics. Public health administrators and managers facing constrained budgets and an increasing demand for accountability will find its practical approach and wealth of public health applications invaluable. Public health decision-making will continue to be a complex mixture of science, politics, and economics, however, the sound technical information provided in this guidebook woll help ground decisions in the public health system in accurate cost estimates and as well as solid science.

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Here at last is a book that puts into perspective the revolution that has taken place in patient care--a revolution that has dethroned the medical establishment and made patients and their families as well as lawyers and even philosophers partners in decisions on treatment.Read more Strangers at the Bedside: A History of How Law and Bioethics Transformed Medical Decision-Making

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This popular reference facilitates diagnostic and therapeutic decision making for a wide range of common and often complex problems faced in outpatient and inpatient medicine. Comprehensive algorithmic decision trees guide you through more than 250 disorders organized by sign, symptom, problem, or laboratory abnormality. The brief text accompanying each algorithm explains the key steps of the decision making process, giving you the clear, clinical guidelines you need to successfully manage even your toughest cases.

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