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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.
This new edition provides a thorough understanding of the key decision making infrastructure of clinical practice and explains the principles of medical decision making both for individual patients and the wider health care 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…mehr
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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.
This new edition provides a thorough understanding of the key decision making infrastructure of clinical practice and explains the principles of medical decision making both for individual patients and the wider health care 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.
Medical Decision Making is a valuable resource for all experienced and learning clinicians who wish to fully understand and apply decision modelling, enhance their practice and improve patient outcomes.
"There is little doubt that in the future many clinical analyses will be based on the methods described in Medical Decision Making, and the book provides a basis for a critical appraisal of such policies." - Jerome P. Kassirer M.D., Distinguished Professor, Tufts University School of Medicine, US and Visiting Professor, Stanford Medical School, U
This new edition provides a thorough understanding of the key decision making infrastructure of clinical practice and explains the principles of medical decision making both for individual patients and the wider health care 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.
Medical Decision Making is a valuable resource for all experienced and learning clinicians who wish to fully understand and apply decision modelling, enhance their practice and improve patient outcomes.
"There is little doubt that in the future many clinical analyses will be based on the methods described in Medical Decision Making, and the book provides a basis for a critical appraisal of such policies." - Jerome P. Kassirer M.D., Distinguished Professor, Tufts University School of Medicine, US and Visiting Professor, Stanford Medical School, U
Produktdetails
- Produktdetails
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14565866000
- 2. Aufl.
- Seitenzahl: 368
- Erscheinungstermin: 29. Juli 2013
- Englisch
- Abmessung: 234mm x 156mm x 20mm
- Gewicht: 50g
- ISBN-13: 9780470658666
- ISBN-10: 0470658665
- Artikelnr.: 36789018
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14565866000
- 2. Aufl.
- Seitenzahl: 368
- Erscheinungstermin: 29. Juli 2013
- Englisch
- Abmessung: 234mm x 156mm x 20mm
- Gewicht: 50g
- ISBN-13: 9780470658666
- ISBN-10: 0470658665
- Artikelnr.: 36789018
Harold C. Sox Geisel School of Medicine at Dartmouth, Hanover, New Hampshire Michael C. Higgins Stanford University, Stanford, California Douglas K. Owens Department of Veterans Affairs Palo Alto Health Care System, Palo Alto, California; Stanford University, Stanford, California
Foreword
xi Preface
xv 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? 2 1.3 How do I interpret new diagnostic information? 5 1.4 How do I select the appropriate diagnostic test? 5 1.5 How do I choose among several risky treatment alternatives? 6 1.6 Summary
6 2 Differential diagnosis
7 2.1 Introduction
7 2.2 How clinicians make a diagnosis
8 2.3 The principles of hypothesis-driven differential diagnosis
11 2.4 An extended example
21 Bibliography
26 3 Probability: quantifying uncertainty
27 3.1 Uncertainty and probability in medicine
27 3.2 Using personal experience to estimate probability
34 3.3 Using published experience to estimate probability
46 3.4 Taking the special characteristics of the patient into account when estimating probability
57 Problems
58 Bibliography
59 4 Understanding new information: Bayes' theorem
61 4.1 Introduction
61 4.2 Conditional probability defined
64 4.3 Bayes' theorem
65 4.4 The odds ratio form of Bayes' theorem
69 4.5 Lessons to be learned from Bayes' theorem
76 4.6 The assumptions of Bayes' theorem
82 4.7 Using Bayes' theorem to interpret a sequence of tests
84 4.8 Using Bayes' theorem when many diseases are under consideration
88 Problems
90 Bibliography
91 5 Measuring the accuracy of diagnostic information
93 5.1 How to describe test results: abnormal and normal
positive and negative
93 5.2 Measuring a test's capability to reveal the patient's true state
98 5.3 Howto measure the characteristics of a diagnostic test: a hypothetical case
106 5.4 Pitfalls of predictive value
109 5.5 Sources of biased estimates of test performance and how to avoid them
110 5.6 Spectrum bias
116 5.7 Expressing test results as continuous variables
125 5.8 Combining data from several studies of test performance
134 Problems
137 Bibliography
140 6 Expected value decision making
143 6.1 An example
145 6.2 Selecting the decision maker
148 6.3 Decision trees: structured representations for decision problems
149 6.4 Quantifying uncertainty
152 6.5 Probabilistic analysis of decision trees
156 6.6 Expected value calculations
158 6.7 Sensitivity analysis
161 6.8 Folding back decision trees
163 Problems
168 Bibliography
168 7 Markov models and time-varying outcomes
170 7.1 Markov model basics
170 7.2 Exponential survival model and life expectancy
189 Problems
198 Appendix: Mathematical details
200 Bibliography
203 8 Measuring the outcome of care - expected utility analysis
204 8.1 Basic concept - direct utility assessment
205 8.2 Sensitivity analysis - testing the robustness of utility analysis
210 8.3 Shortcut - using a linear scale to express strength of preference
212 8.4 Exponential utility - a parametric model
213 8.5 Exponential utility with exponential survival
218 8.6 Multidimensional outcomes - direct assessment
220 8.7 Multidimensional outcomes - simplifications
223 8.8 Multidimensional outcomes - quality-adjusted life years (QALY)
228 8.9 Comparison of the two models for outcomes with different length and quality
232 Problems
235 Appendix: Mathematical details
237 Bibliography
242 9 Selection and interpretation of diagnostic tests
243 9.1 Taking action when the consequences are uncertain: principles and definitions
244 9.2 The treatment-threshold probability
247 9.3 The decision to obtain a diagnostic test
252 9.4 Choosing between diagnostic tests
259 9.5 Choosing the best combination of diagnostic tests
261 9.6 Setting the treatment-threshold probability
263 9.7 Taking account of the utility of experiencing a test
275 9.8 A clinical case: test selection for suspected brain tumor
279 9.9 Sensitivity analysis
281 Bibliography
287 10 Cost-effectiveness analysis and cost-benefit analysis
288 10.1 The clinician's conflicting roles: patient advocate
member of society
and entrepreneur
288 10.2 Cost-effectiveness analysis: a method for comparing management strategies
291 10.3 Cost-benefit analysis: a method for measuring the net benefit of medical services
298 10.4 Measuring the costs of medical care
301 Problems
304 Bibliography
305 11 Medical decision analysis in practice: advanced methods
307 11.1 An overview of advanced modeling techniques
307 11.2 Use of medical decision-making concepts to analyze a policy problem: the cost-effectiveness of screening for HIV
311 11.3 Use of medical decision-making concepts to analyze a clinical diagnostic problem: strategies to diagnose tumors in the lung
323 11.4 Use of complexmodels for individual-patient decisionmaking
330 Bibliography
333 Index
337
xi Preface
xv 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? 2 1.3 How do I interpret new diagnostic information? 5 1.4 How do I select the appropriate diagnostic test? 5 1.5 How do I choose among several risky treatment alternatives? 6 1.6 Summary
6 2 Differential diagnosis
7 2.1 Introduction
7 2.2 How clinicians make a diagnosis
8 2.3 The principles of hypothesis-driven differential diagnosis
11 2.4 An extended example
21 Bibliography
26 3 Probability: quantifying uncertainty
27 3.1 Uncertainty and probability in medicine
27 3.2 Using personal experience to estimate probability
34 3.3 Using published experience to estimate probability
46 3.4 Taking the special characteristics of the patient into account when estimating probability
57 Problems
58 Bibliography
59 4 Understanding new information: Bayes' theorem
61 4.1 Introduction
61 4.2 Conditional probability defined
64 4.3 Bayes' theorem
65 4.4 The odds ratio form of Bayes' theorem
69 4.5 Lessons to be learned from Bayes' theorem
76 4.6 The assumptions of Bayes' theorem
82 4.7 Using Bayes' theorem to interpret a sequence of tests
84 4.8 Using Bayes' theorem when many diseases are under consideration
88 Problems
90 Bibliography
91 5 Measuring the accuracy of diagnostic information
93 5.1 How to describe test results: abnormal and normal
positive and negative
93 5.2 Measuring a test's capability to reveal the patient's true state
98 5.3 Howto measure the characteristics of a diagnostic test: a hypothetical case
106 5.4 Pitfalls of predictive value
109 5.5 Sources of biased estimates of test performance and how to avoid them
110 5.6 Spectrum bias
116 5.7 Expressing test results as continuous variables
125 5.8 Combining data from several studies of test performance
134 Problems
137 Bibliography
140 6 Expected value decision making
143 6.1 An example
145 6.2 Selecting the decision maker
148 6.3 Decision trees: structured representations for decision problems
149 6.4 Quantifying uncertainty
152 6.5 Probabilistic analysis of decision trees
156 6.6 Expected value calculations
158 6.7 Sensitivity analysis
161 6.8 Folding back decision trees
163 Problems
168 Bibliography
168 7 Markov models and time-varying outcomes
170 7.1 Markov model basics
170 7.2 Exponential survival model and life expectancy
189 Problems
198 Appendix: Mathematical details
200 Bibliography
203 8 Measuring the outcome of care - expected utility analysis
204 8.1 Basic concept - direct utility assessment
205 8.2 Sensitivity analysis - testing the robustness of utility analysis
210 8.3 Shortcut - using a linear scale to express strength of preference
212 8.4 Exponential utility - a parametric model
213 8.5 Exponential utility with exponential survival
218 8.6 Multidimensional outcomes - direct assessment
220 8.7 Multidimensional outcomes - simplifications
223 8.8 Multidimensional outcomes - quality-adjusted life years (QALY)
228 8.9 Comparison of the two models for outcomes with different length and quality
232 Problems
235 Appendix: Mathematical details
237 Bibliography
242 9 Selection and interpretation of diagnostic tests
243 9.1 Taking action when the consequences are uncertain: principles and definitions
244 9.2 The treatment-threshold probability
247 9.3 The decision to obtain a diagnostic test
252 9.4 Choosing between diagnostic tests
259 9.5 Choosing the best combination of diagnostic tests
261 9.6 Setting the treatment-threshold probability
263 9.7 Taking account of the utility of experiencing a test
275 9.8 A clinical case: test selection for suspected brain tumor
279 9.9 Sensitivity analysis
281 Bibliography
287 10 Cost-effectiveness analysis and cost-benefit analysis
288 10.1 The clinician's conflicting roles: patient advocate
member of society
and entrepreneur
288 10.2 Cost-effectiveness analysis: a method for comparing management strategies
291 10.3 Cost-benefit analysis: a method for measuring the net benefit of medical services
298 10.4 Measuring the costs of medical care
301 Problems
304 Bibliography
305 11 Medical decision analysis in practice: advanced methods
307 11.1 An overview of advanced modeling techniques
307 11.2 Use of medical decision-making concepts to analyze a policy problem: the cost-effectiveness of screening for HIV
311 11.3 Use of medical decision-making concepts to analyze a clinical diagnostic problem: strategies to diagnose tumors in the lung
323 11.4 Use of complexmodels for individual-patient decisionmaking
330 Bibliography
333 Index
337
Foreword
xi Preface
xv 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? 2 1.3 How do I interpret new diagnostic information? 5 1.4 How do I select the appropriate diagnostic test? 5 1.5 How do I choose among several risky treatment alternatives? 6 1.6 Summary
6 2 Differential diagnosis
7 2.1 Introduction
7 2.2 How clinicians make a diagnosis
8 2.3 The principles of hypothesis-driven differential diagnosis
11 2.4 An extended example
21 Bibliography
26 3 Probability: quantifying uncertainty
27 3.1 Uncertainty and probability in medicine
27 3.2 Using personal experience to estimate probability
34 3.3 Using published experience to estimate probability
46 3.4 Taking the special characteristics of the patient into account when estimating probability
57 Problems
58 Bibliography
59 4 Understanding new information: Bayes' theorem
61 4.1 Introduction
61 4.2 Conditional probability defined
64 4.3 Bayes' theorem
65 4.4 The odds ratio form of Bayes' theorem
69 4.5 Lessons to be learned from Bayes' theorem
76 4.6 The assumptions of Bayes' theorem
82 4.7 Using Bayes' theorem to interpret a sequence of tests
84 4.8 Using Bayes' theorem when many diseases are under consideration
88 Problems
90 Bibliography
91 5 Measuring the accuracy of diagnostic information
93 5.1 How to describe test results: abnormal and normal
positive and negative
93 5.2 Measuring a test's capability to reveal the patient's true state
98 5.3 Howto measure the characteristics of a diagnostic test: a hypothetical case
106 5.4 Pitfalls of predictive value
109 5.5 Sources of biased estimates of test performance and how to avoid them
110 5.6 Spectrum bias
116 5.7 Expressing test results as continuous variables
125 5.8 Combining data from several studies of test performance
134 Problems
137 Bibliography
140 6 Expected value decision making
143 6.1 An example
145 6.2 Selecting the decision maker
148 6.3 Decision trees: structured representations for decision problems
149 6.4 Quantifying uncertainty
152 6.5 Probabilistic analysis of decision trees
156 6.6 Expected value calculations
158 6.7 Sensitivity analysis
161 6.8 Folding back decision trees
163 Problems
168 Bibliography
168 7 Markov models and time-varying outcomes
170 7.1 Markov model basics
170 7.2 Exponential survival model and life expectancy
189 Problems
198 Appendix: Mathematical details
200 Bibliography
203 8 Measuring the outcome of care - expected utility analysis
204 8.1 Basic concept - direct utility assessment
205 8.2 Sensitivity analysis - testing the robustness of utility analysis
210 8.3 Shortcut - using a linear scale to express strength of preference
212 8.4 Exponential utility - a parametric model
213 8.5 Exponential utility with exponential survival
218 8.6 Multidimensional outcomes - direct assessment
220 8.7 Multidimensional outcomes - simplifications
223 8.8 Multidimensional outcomes - quality-adjusted life years (QALY)
228 8.9 Comparison of the two models for outcomes with different length and quality
232 Problems
235 Appendix: Mathematical details
237 Bibliography
242 9 Selection and interpretation of diagnostic tests
243 9.1 Taking action when the consequences are uncertain: principles and definitions
244 9.2 The treatment-threshold probability
247 9.3 The decision to obtain a diagnostic test
252 9.4 Choosing between diagnostic tests
259 9.5 Choosing the best combination of diagnostic tests
261 9.6 Setting the treatment-threshold probability
263 9.7 Taking account of the utility of experiencing a test
275 9.8 A clinical case: test selection for suspected brain tumor
279 9.9 Sensitivity analysis
281 Bibliography
287 10 Cost-effectiveness analysis and cost-benefit analysis
288 10.1 The clinician's conflicting roles: patient advocate
member of society
and entrepreneur
288 10.2 Cost-effectiveness analysis: a method for comparing management strategies
291 10.3 Cost-benefit analysis: a method for measuring the net benefit of medical services
298 10.4 Measuring the costs of medical care
301 Problems
304 Bibliography
305 11 Medical decision analysis in practice: advanced methods
307 11.1 An overview of advanced modeling techniques
307 11.2 Use of medical decision-making concepts to analyze a policy problem: the cost-effectiveness of screening for HIV
311 11.3 Use of medical decision-making concepts to analyze a clinical diagnostic problem: strategies to diagnose tumors in the lung
323 11.4 Use of complexmodels for individual-patient decisionmaking
330 Bibliography
333 Index
337
xi Preface
xv 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? 2 1.3 How do I interpret new diagnostic information? 5 1.4 How do I select the appropriate diagnostic test? 5 1.5 How do I choose among several risky treatment alternatives? 6 1.6 Summary
6 2 Differential diagnosis
7 2.1 Introduction
7 2.2 How clinicians make a diagnosis
8 2.3 The principles of hypothesis-driven differential diagnosis
11 2.4 An extended example
21 Bibliography
26 3 Probability: quantifying uncertainty
27 3.1 Uncertainty and probability in medicine
27 3.2 Using personal experience to estimate probability
34 3.3 Using published experience to estimate probability
46 3.4 Taking the special characteristics of the patient into account when estimating probability
57 Problems
58 Bibliography
59 4 Understanding new information: Bayes' theorem
61 4.1 Introduction
61 4.2 Conditional probability defined
64 4.3 Bayes' theorem
65 4.4 The odds ratio form of Bayes' theorem
69 4.5 Lessons to be learned from Bayes' theorem
76 4.6 The assumptions of Bayes' theorem
82 4.7 Using Bayes' theorem to interpret a sequence of tests
84 4.8 Using Bayes' theorem when many diseases are under consideration
88 Problems
90 Bibliography
91 5 Measuring the accuracy of diagnostic information
93 5.1 How to describe test results: abnormal and normal
positive and negative
93 5.2 Measuring a test's capability to reveal the patient's true state
98 5.3 Howto measure the characteristics of a diagnostic test: a hypothetical case
106 5.4 Pitfalls of predictive value
109 5.5 Sources of biased estimates of test performance and how to avoid them
110 5.6 Spectrum bias
116 5.7 Expressing test results as continuous variables
125 5.8 Combining data from several studies of test performance
134 Problems
137 Bibliography
140 6 Expected value decision making
143 6.1 An example
145 6.2 Selecting the decision maker
148 6.3 Decision trees: structured representations for decision problems
149 6.4 Quantifying uncertainty
152 6.5 Probabilistic analysis of decision trees
156 6.6 Expected value calculations
158 6.7 Sensitivity analysis
161 6.8 Folding back decision trees
163 Problems
168 Bibliography
168 7 Markov models and time-varying outcomes
170 7.1 Markov model basics
170 7.2 Exponential survival model and life expectancy
189 Problems
198 Appendix: Mathematical details
200 Bibliography
203 8 Measuring the outcome of care - expected utility analysis
204 8.1 Basic concept - direct utility assessment
205 8.2 Sensitivity analysis - testing the robustness of utility analysis
210 8.3 Shortcut - using a linear scale to express strength of preference
212 8.4 Exponential utility - a parametric model
213 8.5 Exponential utility with exponential survival
218 8.6 Multidimensional outcomes - direct assessment
220 8.7 Multidimensional outcomes - simplifications
223 8.8 Multidimensional outcomes - quality-adjusted life years (QALY)
228 8.9 Comparison of the two models for outcomes with different length and quality
232 Problems
235 Appendix: Mathematical details
237 Bibliography
242 9 Selection and interpretation of diagnostic tests
243 9.1 Taking action when the consequences are uncertain: principles and definitions
244 9.2 The treatment-threshold probability
247 9.3 The decision to obtain a diagnostic test
252 9.4 Choosing between diagnostic tests
259 9.5 Choosing the best combination of diagnostic tests
261 9.6 Setting the treatment-threshold probability
263 9.7 Taking account of the utility of experiencing a test
275 9.8 A clinical case: test selection for suspected brain tumor
279 9.9 Sensitivity analysis
281 Bibliography
287 10 Cost-effectiveness analysis and cost-benefit analysis
288 10.1 The clinician's conflicting roles: patient advocate
member of society
and entrepreneur
288 10.2 Cost-effectiveness analysis: a method for comparing management strategies
291 10.3 Cost-benefit analysis: a method for measuring the net benefit of medical services
298 10.4 Measuring the costs of medical care
301 Problems
304 Bibliography
305 11 Medical decision analysis in practice: advanced methods
307 11.1 An overview of advanced modeling techniques
307 11.2 Use of medical decision-making concepts to analyze a policy problem: the cost-effectiveness of screening for HIV
311 11.3 Use of medical decision-making concepts to analyze a clinical diagnostic problem: strategies to diagnose tumors in the lung
323 11.4 Use of complexmodels for individual-patient decisionmaking
330 Bibliography
333 Index
337