Medical Decision Making, 3e (Feb 27, 2024)_(111962780X)_(Wiley-Blackwell)

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. 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 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 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. "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)"-- Provided by publisher