Overview

"MEDICAL DECISION MAKING Detailed resource showing how to best make medical decisions while incorporating clinical practice guidelines and decision support systems Sir 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 qualified authors, Medical Decision Making includes information on: How to consider the possible causes of a patient’s illness and decide on the probability of the most important diagnoses. How to measure the accuracy of a diagnostic test. How to help patients express their concerns about the risks that they face and how an illness may affect their lives. How to describe uncertainty about how an illness may change over time. How to construct and analyze decision trees. How to identify the threshold for doing a test or starting treatment How to apply these concepts to the design of practice guidelines and medical policy making. Medical Decision Making is a valuable resource for clinicians, medical trainees, and students of decision analysis who wish to fully understand and apply the principles of decision making to clinical practice."

Full Product Details

Author:   Harold C. Sox, Jr. (Geisel School of Medicine at Dartmouth, USA) ,  Michael C. Higgins (Biomedical Informatics Research, Stanford University, USA) ,  Douglas K. Owens (Stanford University, USA) ,  Gillian Sanders Schmidler (Duke-Margolis Institute for Health Policy, Durham, USA)
Publisher:   John Wiley and Sons Ltd
Imprint:   Wiley-Blackwell
Edition:   3rd edition
Dimensions:   Width: 21.30cm , Height: 1.80cm , Length: 27.40cm
Weight:   0.975kg
ISBN:  

9781119627807

ISBN 10:   111962780
Pages:   368
Publication Date:   29 February 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In stock  
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Table 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 160 9.3 Incorporating risk attitudes into clinical policies 172 9.4 Helping patients communicate their preferences 181 Epilogue 185 A.9.1 Exponential utility model parameter nomogram 186 Bibliography 188 10 Outcome utilities – adjusting for the quality of life 189 10.1 Introduction 189 10.2 Example – why the quality of life matters 190 10.3 Quality- lifetime tradeoff models 193 10.4 Quality- survival tradeoff models 203 10.5 What does it all mean? – an extended example 209 Epilogue 217 Bibliography 217 11 Survival models: representing uncertainty about the length of life 218 11.1 Introduction 218 11.2 Survival model basics 219 11.3 Medical example – survival after breast cancer recurrence 226 11.4 Exponential survival model 228 11.5 Actuarial survival models 232 11.6 Two- part survival models 235 Epilogue 247 Bibliography 247 12 Markov models 248 12.1 Introduction 248 12.2 Markov model basics 249 12.3 Determining transition probabilities 259 12.4 Markov model analysis – an overview 269 Epilogue 277 Bibliography 277 13 Selection and interpretation of diagnostic tests 278 13.1 Introduction 278 13.2 Four principles of decision making 279 13.3 The threshold probability for treatment 281 13.4 Threshold probabilities for testing 288 13.5 Clinical application of the threshold model of decision making 293 13.6 Accounting for the non- diagnostic effects of undergoing a test 296 13.7 Sensitivity analysis 298 13.8 Decision curve analysis 300 Bibliography 302 14 Medical decision analysis in practice: advanced methods 303 14.1 An overview of advanced modeling techniques 303 14.2 Use of medical decision- making concepts to analyze a policy problem: the cost- effectiveness of screening for HIV 305 14.3 Use of medical decision- making concepts to analyze a clinical diagnostic problem: strategies to diagnose tumors in the lung 313 14.4 Calibration and validation of decision models 317 14.5 Use of complex models for individual- patient decision making 319 Bibliography 321 15 Cost- effectiveness analysis 323 15.1 The clinician’s conflicting roles: patient advocate member of society and entrepreneur 323 15.2 Cost- effectiveness analysis: a method for comparing management strategies 325 15.3 Cost–benefit analysis: a method for measuring the net benefit of medical services 330 15.4 Methodological best practices for cost- effectiveness analysis 332 15.5 Reference case for cost- effectiveness analysis 333 15.6 Impact inventory for cataloguing consequences 334 15.7 Measuring the health effects of medical care 334 15.8 Measuring the costs of medical care 335 15.9 Interpretation of cost- effectiveness analysis and use in decision making 337 15.10 Limitations of cost- effectiveness analyses 337 Bibliography 338 Index 340

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Author Information

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.

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