Medical Decision Making

✍ Scribed by Harold C. Sox, Michael C. Higgins, Douglas K. Owens, Gillian Sanders Schmidler

• Publisher: Wiley-Blackwell
• Year: 2024
• Tongue: English
• Leaves: 365
• Edition: 3
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Title Page
Copyright Page
Dedication Page
Contents
Foreword
Preface
CHAPTER 1 Introduction
1.1 How may I be thorough yet efficient when considering the possible causes of my patient’s problems?
1.2 How do I characterize the information I have gathered during the medical interview and physical examination?
1.3 How do I interpret new diagnostic information?
1.4 How do I select the appropriate diagnostic test?
1.5 How do I choose among several risky treatment alternatives?
CHAPTER 2 Differential diagnosis
2.1 An introduction
2.2 How clinicians make a diagnosis
2.3 The principles of hypothesis-driven differential diagnosis
2.3.1 The first step in differential diagnosis: listening and generating hypotheses
2.3.2 The second step in differential diagnosis: gathering data to test hypotheses
2.3.3 Hypothesis testing
2.3.4 Selecting a course of action
2.4 An extended example
2.4.1 Clinical aphorisms
Bibliography
CHAPTER 3 Probability: quantifying uncertainty
3.1 Uncertainty and probability in medicine
3.1.1 The uncertain nature of clinical information
3.1.2 Definition and key concepts
3.1.3 The meaning of probability: the present state vs. a future event
3.1.4 Odds: an alternative way to express a probability
3.2 How to determine a probability
3.2.1 Probability: a quantification of judgment about the likelihood of an event
3.2.2 Indirect probability assessment
3.2.3 Direct probability assessment
3.3 Sources of error in using personal experience to estimate the probability
3.3.1 Heuristics defined
3.3.2 Heuristic I: representativeness
3.3.3 Heuristic II: availability
3.3.4 Heuristic III: anchoring and adjustment
3.3.5 Correctly using heuristics for estimating probability
3.4 The role of empirical evidence in quantifying uncertainty
3.4.1 Determining probability from the prevalence of disease in patients with a symptom, physical finding, or test result
3.4.2 Determining the probability of a disease from its prevalence in patients with a clinical syndrome
3.4.3 Establishing a probability using a clinical prediction model
3.5 Limitations of published studies of disease prevalence
3.5.1 Caution in using published reports to determine probability
3.6 Taking the special characteristics of the patient into account when determining probabilities
Bibliography
CHAPTER 4 Interpreting new information: Bayes’ theorem
4.1 Introduction
4.2 Conditional probability defined
4.3 Bayes’ theorem
4.3.1 Derivation of Bayes’ theorem
4.3.2 Clinically useful forms of Bayes’ theorem
4.4 The odds ratio form of Bayes’ theorem
4.4.1 The derivation of the odds ratio form of Bayes’ theorem
4.4.2 The likelihood ratio: a measure of test discrimination
4.4.3 Using the odds ratio form of Bayes’ theorem
4.5 Lessons to be learned from using Bayes’ theorem
4.5.1 Further thoughts
4.5.2 The clinical significance of test specificity
4.5.3 The clinical significance of test sensitivity
4.6 The assumptions of Bayes’ theorem
4.7 Using Bayes’ theorem to interpret a sequence of tests
4.8 Using Bayes’ theorem when many diseases are under consideration
Bibliography
CHAPTER 5 Measuring the accuracy of clinical findings
5.1 A language for describing test results
5.1.1 Defining a test result
5.2 The measurement of diagnostic test performance
5.2.1 How to measure test performance
5.2.2 Measures of concordance between index test and disease state
5.2.3 Measures of discordance between index test and disease state
5.2.4 Predictive value
5.3 How to measure diagnostic test performance: a hypothetical example
5.3.1 Description of the study
5.3.2 Description of results
5.3.3 An important limitation of the spleen scan study
5.4 Pitfalls of predictive value
5.5 How to perform a high quality study of diagnostic test performance
5.5.1 The features of a high-quality prospective study of a diagnostic test
5.5.2 Study characteristics that help ensure that the results apply to usual practice
5.5.3 Study characteristics that insure unbiased, reproducible interpretation of the index test and the gold standard test
5.6 Spectrum bias in the measurement of test performance
5.6.1 The first phase of test evaluation: testing the “sickest of the sick” and the “wellest of the well”
5.6.2 The second phase of test evaluation: reluctance to order the gold standard test because of over-confidence in a negative index test result
5.6.3 Effects of spectrum bias
5.6.4 Adjusting for biased estimates of sensitivity and specificity
5.6.5 Heuristics for adjusting published reports for disease severity bias
5.7 When to be concerned about inaccurate measures of test performance
5.8 Test results as a continuous variable: the ROC curve
5.8.1 The distribution of test results in diseased and well individuals
5.8.2 The receiver operating characteristic curve
5.8.3 Using the ROC curve to compare tests
5.8.4 Setting the cut point for a test
5.9 Combining data from studies of test performance: the systematic review and meta-analysis
A.5.1 Appendix: derivation of the method for using an ROC curve to choose the definition of an abnormal test result
Bibliography
CHAPTER 6 Decision trees – representing the structure of a decision problem
6.1 Introduction
6.2 Key concepts and terminology
6.2.1 Final outcomes
6.2.2 Branch probabilities and outcome probabilities
6.2.3 Expected value calculations and life expectancy
6.3 Constructing the decision tree for a hypothetical decision problem
6.4 Constructing the decision tree for a medical decision problem
6.4.1 Management of coronary artery disease overview
6.4.2 Simple decision in the management of coronary artery disease
6.4.3 Determining the branch probabilities
6.4.4 Alternate chance node ordering
6.4.5 Computing the life expectancy for the decision alternatives
Epilogue
Bibliography
CHAPTER 7 Decision tree analysis
7.1 Introduction
7.2 Folding-back operation
7.2.1 Folding-back operation applied to hypothetical problem
7.2.2 Chance node ordering revisited
7.2.3 Two-stage decision in the management of coronary artery disease
7.2.4 Decision tree for two-stage coronary artery disease management decision
7.2.5 Folding-back operation applied to two-stage coronary artery disease decision problem
7.2.6 Conclusion of the folding-back operation
7.2.7 Comment on number of significant figures used in calculations
7.3 Sensitivity analysis
7.3.1 One-way sensitivity analysis for simple decision problems
7.3.2 Two-way sensitivity analysis for simple decision problems
7.3.3 Sensitivity analysis for problems with two decisions
7.3.4 Sensitivity analysis and clinical policies
Epilogue
Bibliography
CHAPTER 8 Outcome utility – representing risk attitudes
8.1 Introduction
8.2 What are risk attitudes?
8.2.1 Risk-tolerant preferences
8.3 Demonstration of risk attitudes in a medical context
8.3.1 Depicting choice of lung cancer treatment as a decision tree
8.3.2 Branch probabilities for the lung cancer treatment decision
8.3.3 von Neumann-Morgenstern utility and the outcome values
8.3.4 Using standard gamble assessment questions to determine outcome utilities
8.3.5 Determining the outcome utilities for the lung cancer decision problem
8.3.6 Computing Patient A’s expected utility for each of the treatments
8.3.7 Risk attitudes matter
8.4 General observations about outcome utilities
8.4.1 Certainty equivalent – providing a tangible meaning for expected utility analysis
8.4.2 Risk attitudes revisited
8.5 Determining outcome utilities – underlying concepts
8.5.1 Lifetime-tradeoff assessment
8.5.2 Survival-tradeoff assessment
Epilogue
Bibliography
CHAPTER 9 Outcome utilities – clinical applications
9.1 Introduction
9.2 A parametric model for outcome utilities
9.2.1 What is a parametric model?
9.2.2 The exponential utility model
9.2.3 Scaling exponential utility models
9.2.4 Assumption underlying the exponential utility model
9.2.5 Determining the exponential utility model parameter – first approach
9.2.6 Determining the exponential utility model parameter – alternate assessment approach
9.2.7 Exponential utility model parameter and risk attitudes
9.3 Incorporating risk attitudes into clinical policies
9.3.1 Risk-adjusted clinical policies – underlying concept
9.3.2 Clinical context for illustrating risk-adjusted clinical policy design
9.3.3 Determining the risk parameter threshold
9.3.4 A simpler assessment question
9.3.5 Generalized age- and gender-specific clinical policy
9.3.6 Risk-adjusted clinical policies – what does it all mean?
9.4 Helping patients communicate their preferences
Epilogue
A.9.1 Exponential utility model parameter nomogram
Bibliography
CHAPTER 10 Outcome utilities – adjusting for the quality of life
10.1 Introduction
10.2 Example – why the quality of life matters
10.3 Quality-lifetime tradeoff models
10.3.1 Parameterizing the quality-lifetime tradeoff model
10.3.2 Quality-lifetime parametric utility model with constant risk attitudes
10.3.3 Quality-lifetime tradeoff models and risk aversion – a fly in the ointment
10.3.4 Quality-lifetime tradeoff modelling and healthcare policy analysis
10.4 Quality-survival tradeoff models
10.4.1 Assessing quality preferences with the quality-survival tradeoff model
10.4.2 Parameterized quality-survival tradeoff model
10.4.3 Parameterized quality-survival tradeoff model and exponential survival
10.5 What does it all mean? – an extended example
10.5.1 Direct approach to outcome utility assessment
10.5.2 Outcome utility assessment based on outcome decomposition
Epilogue
Bibliography
CHAPTER 11 Survival models: representing uncertainty about the length of life
11.1 Introduction
11.2 Survival model basics
11.2.1 Survival probabilities
11.2.2 Lifetime probabilities
11.2.3 Lifetime probabilities and the representation of time
11.2.4 Hazard rates
11.2.5 Estimating a survival model from observations
11.2.6 Kaplan–Meier survival model
11.3 Medical example – survival after breast cancer recurrence
11.4 Exponential survival model
11.4.1 Lifetime probabilities with the exponential survival model
11.4.2 Fitting an exponential survival model to observations – first attempt
11.5 Actuarial survival models
11.5.1 Age- and gender-specific actuarial survival models
11.5.2 Further adjustments of the actuarial survival model
11.6 Two-part survival models
11.6.1 Representing observed survival with an exponential survival model – second attempt
11.6.2 Age adjusting a survival model
11.6.3 Computing outcome utilities with the parametric two-part survival model
11.6.4 Limitations
Epilogue
Bibliography
CHAPTER 12 Markov models
12.1 Introduction
12.2 Markov model basics
12.2.1 Health states and transition probabilities
12.2.2 Markov model diagrams and notation
12.2.3 Markov independence
12.2.4 Stationarity assumption
12.2.5 Acyclic graph assumption
12.3 Determining transition probabilities
12.3.1 Markov model used to illustrate how transition probabilities are determined
12.3.2 Determining mortality rates
12.3.3 Determining probability for transitions between stages of recurrence
12.3.4 Determining transition probabilities for treatment response
12.4 Markov model analysis – an overview
12.4.1 Direct approach to Markov model analysis
12.4.2 Using Monte Carlo simulation to analyze Markov models
Epilogue
Bibliography
CHAPTER 13 Selection and interpretation of diagnostic tests
13.1 Introduction
13.2 Four principles of decision making
13.2.1 Two examples of decision making under uncertainty
13.2.2 The four principles as a framework
13.3 The threshold probability for treatment
13.3.1 The rationale for a treatment threshold probability
13.3.2 Deriving an expression for the treatment threshold probability
13.3.3 Heuristics for setting a treatment threshold probability
13.3.4 Determining the treatment threshold probability for pulmonary embolism – a formal approach
13.4 Threshold probabilities for testing
13.4.1 The criteria for doing a test
13.4.2 A method for deciding when to perform a diagnostic test
13.4.3 Equations for calculating testing thresholds
13.5 Clinical application of the threshold model of decision making
13.5.1 Test selection for suspected pulmonary embolism: an example
13.5.2 Incorporating a clinical prediction model into a probabilistic framework for test selection for suspected pulmonary embolism
13.6 Accounting for the non-diagnostic effects of undergoing a test
13.7 Sensitivity analysis
13.8 Decision curve analysis
13.8.1 Making the plot of net benefit vs. p*
13.8.2 Use of DCA in practice
Bibliography
CHAPTER 14 Medical decision analysis in practice: advanced methods
14.1 An overview of advanced modeling techniques
14.1.1 When are advanced modeling approaches needed?
14.1.2 Types of modeling approaches
14.1.3 Choosing among modeling approaches
14.2 Use of medical decision‐making concepts to analyze a policy problem: the cost‐effectiveness of screening for HIV
14.2.1 The policy question
14.2.2 Steps of the analysis
14.2.3 Define the problem, objectives, and perspective
14.2.4 Identify alternatives and choose the modeling framework
14.2.5 Structure the problem, define chance events, represent the time sequence
14.2.6 Determine the probability of chance events
14.2.7 Value the outcomes
14.2.8 Estimate costs and discount outcomes
14.2.9 Calculate the expected utility, costs, and cost‐effectiveness
14.2.10 Evaluate uncertainty
14.2.11 Address ethical issues, discuss results
14.3 Use of medical decision‐making concepts to analyze a clinical diagnostic problem: strategies to diagnose tumors in the lung
14.3.1 Define the problem, objectives, and perspective
14.3.2 Identify alternatives and choose the modeling framework
14.3.3 Structure the problem, define chance events, and represent the time sequence
14.3.4 Determine the probability of the chance events
14.3.5 Value the outcomes
14.3.6 Estimate costs and discount outcomes
14.3.7 Calculate expected utility, costs, and cost‐effectiveness
14.3.8 Evaluate uncertainty
14.3.9 Address ethical issues, discuss results
14.4 Calibration and validation of decision models
14.5 Use of complex models for individual‐patient decision making
14.5.1 The Alchemist decision support system
14.5.2 Challenges for individual‐patient decision making
Bibliography
CHAPTER 15 Cost-effectiveness analysis
15.1 The clinician’s conflicting roles: patient advocate, member of society, and entrepreneur
15.1.1 Principles of allocating scarce resources
15.2 Cost-effectiveness analysis: a method for comparing management strategies
15.2.1 Using cost-effectiveness analysis to set institutional policy: an extended example
15.2.2 Flat-of-the-curve medicine
15.3 Cost–benefit analysis: a method for measuring the net benefit of medical services
15.3.1 The distinction between cost–benefit analysis and cost-effectiveness analysis
15.3.2 Placing a monetary value on human life
15.3.3 Should clinicians take an interest in cost–benefit analysis?
15.4 Methodological best practices for cost-effectiveness analysis
15.5 Reference case for cost-effectiveness analysis
15.6 Impact inventory for cataloguing consequences
15.7 Measuring the health effects of medical care
15.8 Measuring the costs of medical care
15.9 Interpretation of cost-effectiveness analysis and use in decision making
15.10 Limitations of cost-effectiveness analyses
Bibliography
Index
EULA