Details

Medical Decision Making

3. Aufl.

von: Harold C. Sox, Michael C. Higgins, Douglas K. Owens, Gillian Sanders Schmidler
47,99 €
Verlag:	Wiley-Blackwell
Format:	PDF
Veröffentl.:	05.02.2024
ISBN/EAN:	9781119627845
Sprache:	englisch
Anzahl Seiten:	368

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Titelbeschreibung

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: <ul><li>How to consider the possible causes of a patient’s illness and decide on the probability of the most important diagnoses.</li> <li>How to measure the accuracy of a diagnostic test.</li> <li>How to help patients express their concerns about the risks that they face and how an illness may affect their lives.</li> <li>How to describe uncertainty about how an illness may change over time.</li> <li>How to construct and analyze decision trees.</li> <li>How to identify the threshold for doing a test or starting treatment</li> <li>How to apply these concepts to the design of practice guidelines and medical policy making.</li></ul> 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.

Inhaltsverzeichnis

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 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 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 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 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 7 Decision tree analysis, 113 7.1 Introduction, 113 7.2 Folding-back operation, 114 7.3 Sensitivity analysis, 126 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 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 A.9.1 Exponential utility model parameter nomogram, 186 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 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 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 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 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 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

Autorenportrait

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.

Back cover copy

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: <ul><li>How to consider the possible causes of a patient’s illness and decide on the probability of the most important diagnoses.</li> <li>How to measure the accuracy of a diagnostic test.</li> <li>How to help patients express their concerns about the risks that they face and how an illness may affect their lives.</li> <li>How to describe uncertainty about how an illness may change over time.</li> <li>How to construct and analyze decision trees.</li> <li>How to identify the threshold for doing a test or starting treatment</li> <li>How to apply these concepts to the design of practice guidelines and medical policy making.</li></ul> 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.