Medical Decision Making

Informationen zum Autor Harold C. Sox is Emeritus Professor of Medicine and of the Dartmouth Institute at Geisel School of Medicine at Dartmouth, USA. Michael C. Higgins is Adjunct Professor at the Stanford Center for Biomedical Informatics Research, Stanford University, USA. Douglas K. Owens is a general internist and Professor and Chair of the Department of Health Policy, School of Medicine, and Director of Stanford Health Policy, Freeman-Spogli Institute for International Studies, Stanford University, USA. Gillian Sanders Schmidler is Professor of Population Health Sciences and Medicine at Duke University and Deputy Director of the Duke-Margolis Institute for Health Policy, Durham, USA. Klappentext Detailed resource showing how to best make medical decisions while incorporating clinical practice guidelines and decision support systemsMedical Decision Making provides clinicians with a powerful framework for helping patients make decisions that increase the likelihood that they will have the outcomes that are most consistent with their preferences. The text provides a thorough understanding of the key decision-making infrastructure of clinical practice and explains the principles of medical decision making for both individual patients and the wider healthcare arena. It shows how to make the best clinical decisions based on the available evidence and how to use clinical guidelines and decision support systems in electronic medical records to shape practice guidelines and policies.This newly revised and updated Third Edition includes updates throughout the text, especially concerning new developments in big data. Theory on writing guidelines is included as a practical tool for practitioners in the field.Written by three distinguished and highly qualified authors, Medical Decision Making includes information on:* How to be consider possible causes of a patient's problems, and how to characterize information gathered during medical interviews and physical examinations* Bayes' theorem, covering its assumption, using it to interpret a sequence of tests, and using it when many diseases are under consideration* How to describe test results (abnormal and normal, positive and negative), and measuring a test's capability to reveal the patient's true state* Decisions trees, selecting a decision maker, quantifying uncertainty, expected value calculations, and sensitivity analysisMedical Decision Making is a valuable resource for a wide range of general practitioners and clinicians, as well as medical trainees at intermediate and advanced levels, who wish to fully understand and apply decision modeling, enhance their practice, and improve patient outcomes. Zusammenfassung MEDICAL DECISION MAKINGDetailed resource showing how to best make medical decisions while incorporating clinical practice guidelines and decision support systemsSir William Osler, a legendary physician of an earlier era, once said, "Medicine is a science of uncertainty and an art of probability." In Osler's day, and now, decisions about treatment often cannot wait until the diagnosis is certain. Medical Decision Making is about how to make the best possible decision given that uncertainty. The book shows how to tailor decisions under uncertainty to achieve the best outcome based on published evidence, features of a patient's illness, and the patient's preferences.Medical Decision Making describes a powerful framework for helping clinicians and their patients reach decisions that lead to outcomes that the patient prefers. That framework contains the key principles of patient-centered decision-making in clinical practice.Since the first edition of Medical Decision Making in 1988, the authors have focused on explaining key concepts and illustrating them with clinical examples. For the Third Edition, every chapter has been revised and updated.Written by four distinguished and highly q...

List of contents

Foreword xi
 
Preface xiii
 
1 Introduction 1
 
1.1 How may I be thorough yet efficient when considering the possible causes of my patient's problems? 1
 
1.2 How do I characterize the information I have gathered during the medical interview and physical examination? 1
 
1.3 How do I interpret new diagnostic information? 3
 
1.4 How do I select the appropriate diagnostic test? 4
 
1.5 How do I choose among several risky treatment alternatives? 4
 
2 Differential diagnosis 5
 
2.1 An introduction 5
 
2.2 How clinicians make a diagnosis 5
 
2.3 The principles of hypothesis- driven differential diagnosis 8
 
2.4 An extended example 14
 
Bibliography 16
 
3 Probability: quantifying uncertainty 18
 
3.1 Uncertainty and probability in medicine 18
 
3.2 How to determine a probability 21
 
3.3 Sources of error in using personal experience to estimate the probability 23
 
3.4 The role of empirical evidence in quantifying uncertainty 30
 
3.5 Limitations of published studies of disease prevalence 35
 
3.6 Taking the special characteristics of the patient into account when determining probabilities 36
 
Bibliography 37
 
4 Interpreting new information: Bayes' theorem 38
 
4.1 Introduction 38
 
4.2 Conditional probability defined 40
 
4.3 Bayes' theorem 41
 
4.4 The odds ratio form of Bayes' theorem 45
 
4.5 Lessons to be learned from using Bayes' theorem 50
 
4.6 The assumptions of Bayes' theorem 52
 
4.7 Using Bayes' theorem to interpret a sequence of tests 54
 
4.8 Using Bayes' theorem when many diseases are under consideration 55
 
Bibliography 57
 
5 Measuring the accuracy of clinical findings 58
 
5.1 A language for describing test results 58
 
5.2 The measurement of diagnostic test performance 62
 
5.3 How to measure diagnostic test performance: a hypothetical example 67
 
5.4 Pitfalls of predictive value 69
 
5.5 How to perform a high quality study of diagnostic test performance 70
 
5.6 Spectrum bias in the measurement of test performance 74
 
5.7 When to be concerned about inaccurate measures of test performance 79
 
5.8 Test results as a continuous variable: the ROC curve 81
 
5.9 Combining data from studies of test performance: the systematic review and meta- analysis 87
 
A.5.1 Appendix: derivation of the method for using an ROC curve to choose the definition of an abnormal test result 89
 
Bibliography 91
 
6 Decision trees - representing the structure of a decision problem 93
 
6.1 Introduction 93
 
6.2 Key concepts and terminology 93
 
6.3 Constructing the decision tree for a hypothetical decision problem 96
 
6.4 Constructing the decision tree for a medical decision problem 103
 
Epilogue 112
 
Bibliography 112
 
7 Decision tree analysis 113
 
7.1 Introduction 113
 
7.2 Folding- back operation 114
 
7.3 Sensitivity analysis 126
 
Epilogue 133
 
Bibliography 133
 
8 Outcome utility - representing risk attitudes 134
 
8.1 Introduction 134
 
8.2 What are risk attitudes? 135
 
8.3 Demonstration of risk attitudes in a medical context 136
 
8.4 General observations about outcome utilities 147
 
8.5 Determining outcome utilities - underlying concepts 151
 
Epilogue 157
 
Bibliography 158
 
9 Outcome utilities - clinical applications 159
 
9.1 Introduction 159
 
9.2 A parametric model for outcome utilities

Product details

• Authors: Michael C. Higgins, Higgins Michael C., Douglas K. Owens, Owens Douglas K., Gillian Sanders Schmidler, Harold C. Sox, Sox Harold C.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Paperback / Softback
• Released: 29.02.2024
• EAN: 9781119627807
• ISBN: 978-1-119-62780-7
• No. of pages: 368
• Subjects: Natural sciences, medicine, IT, technology > Medicine > General; Medizin, Medical Science, KLINISCHE FERTIGKEITEN, Medical Professional Development, Perspektiven in medizinischen Berufen, Gesundheitspolitik, Risiken, Sicherheit des Patienten, Health Policy, Health Risk & Patient Safety, Clinical Skills, Klinische Entscheidungsfindung