Atnaujinkite slapukų nuostatas

Medical Decision Making 3rd edition [Minkštas viršelis]

4.00/5 (2 ratings by Goodreads)
(Biomedical Informatics Research, Stanford University, USA), (Stanford University, USA), (Geisel School of Medicine at Dartmouth, USA), (Duke-Margolis Institute for Health Policy, Durham, USA)
  • Formatas: Paperback / softback, 368 pages, aukštis x plotis x storis: 274x213x20 mm, weight: 975 g
  • Išleidimo metai: 29-Feb-2024
  • Leidėjas: Wiley-Blackwell
  • ISBN-10: 111962780X
  • ISBN-13: 9781119627807
Kitos knygos pagal šią temą:
Medical Decision Making 3rd editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 368 pages, aukštis x plotis x storis: 274x213x20 mm, weight: 975 g
  • Išleidimo metai: 29-Feb-2024
  • Leidėjas: Wiley-Blackwell
  • ISBN-10: 111962780X
  • ISBN-13: 9781119627807
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781119627807.html
Raktažodžiai:
"This volume addresses these needs. Chapters 1 and 2 set the stage: uncertainty is everywhere in clinical practice, yet clinical reasoning depends on logical deduction as exemplified by differential diagnosis. Chapters 3, 4, and 5 are about defining and navigating uncertainty: determining probability, updating probability, and the determinants of post-test probability, all basic tools of the clinician. Chapters 6 and 7 are about modeling the factors that shape decisions. Chapters 8-12 explore in-depth the measurement of utility, both the basics and the underlying theory. Topics include attitudes toward taking risks, the quality of life, and the length of life. The last three chapters are about making decisions: deciding when to treat, when to test, and when to wait (Chapter 13); the advanced modeling methods that inform policy. (Chapter 14); and cost-effectiveness analysis (Chapter 15)"--

Detailed resource showing how to best make medical decisions while incorporating clinical practice guidelines and decision support systems

Medical 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 analysis

Medical 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.

Foreword xi

Preface xiii

1 Introduction 1

1.1 How may I be thorough yet efficient when considering the possible causes
of my patients 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 clinicians conflicting roles: patient advocate member of society
and entrepreneur 323

15.2 Cost- effectiveness analysis: a method for comparing management
strategies 325

15.3 Costbenefit 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
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.