Medical decision making / / Harold C. Sox, Michael C. Higgins, Douglas K. Owens, Gillian Sanders Schmidler

• Autore: Sox Harold C
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: Third edition
• Descrizione fisica: 1 online resource (365 pages)
• Disciplina: 616.07/5
• Soggetto topico: Decision support systems; Diagnosis
• Altri autori: HigginsMichael C <1950.> (Michael Clark) OwensDouglas K Sanders SchmidlerGillian
• Nota di contenuto: 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.
• Sommario/riassunto: This book, 'Medical Decision Making', is a comprehensive guide on the principles and applications of decision-making processes in the medical field. Authored by Harold C. Sox, Michael C. Higgins, Douglas K. Owens, and Gillian Sanders Schmidler, the book is designed for healthcare professionals who seek to enhance their decision-making skills. It covers topics such as differential diagnosis, probability and uncertainty, the use of Bayes' theorem, and decision trees. The authors aim to equip clinicians with the tools to interpret diagnostic information, measure clinical findings, and incorporate risk attitudes and outcome utilities into clinical practice. The book also explores survival models and Markov models, providing a detailed framework for understanding and applying these concepts in medical decision-making. It serves as a valuable resource for medical practitioners, students, and policy-makers interested in the intersection of health policy and clinical decision support.
• Titolo autorizzato: Medical decision making
• ISBN: 9781119627722; 1119627729
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911018962003321
• Lo trovi qui: Univ. Federico II