Medical Decision Making

by Sox, Harold C.; Higgins, Michael C.; Owens, Douglas K.

• ISBN13: 9781119627807
• ISBN10: 111962780X
• Edition: 3rd
• Format: Paperback
• Copyright: 2024-01-09
• Publisher: Wiley-Blackwell

Summary

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.

Author Biography

Harold C. Sox is Emeritus Professor of Medicine and of The Dartmouth Institute at Dartmouth Medical School, USA. Douglas K. Owens is general internist and a Senior Investigator at the Center for Health Care Evaluation at the VA Health Care System, Palo Alto, USA and a Professor of Medicine and of Health Research and Policy at Stanford University, USA. Michael Higgins is Consulting Associate Professor at the Stanford Center for Biomedical Informatics Research, Palo Alto, USA.

Table of Contents

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 may I be thorough yet efficient when considering the possible causes of my patient's problems?

1.3  How do I select the appropriate diagnostic test?

1.4   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

2.4   An illustrative example

      Bibliography

CHAPTER 3. PROBABILITY: QUANTIFYING UNCERTAINTY

3.1 Uncertainty and probability in medicine

3.2  How to determine a probability

3.3          Sources of error in using personal experience to estimate probability

 

3.4 The role for empirical evidence in understanding uncertainty

Bibliography

CHAPTER 4. INTERPRETING NEW INFORMATION: BAYES’ THEOREM

4.1 Conditional probability defined

4.2          Bayes' theorem

 

4.3          Odds ratio form of Bayes' theorem

4.4          Lessons to be learned from Bayes' theorem

4.5          The assumptions of Bayes’ theorem

4.6          Using Bayes' theorem to interpret a sequence of tests

4.7          Using Bayes' theorem when many diseases are under consideration

 

Bibliography

CHAPTER 5. MEASURING THE ACCURACY OF CLINICAL FINDINGS

5.1 A language to describe test results

5.2          Measuring a test’s capability to reveal the patient’s true state

 

5.3          How to measure test performance: a hypothetical case

 

5.4          Pitfalls of predictive value

5.5          Sources of bias in estimates of test performance and how to avoid them

5.6          How to adjust for bias in measuring sensitivity and specificity

5.7          When to be concerned about inaccurate measures of test performance

5.8 Expressing test results as continuous variables: the ROC curve

5.9          Combining data from several studies of test performance

Bibliography

CHAPTER 6. DECISION TREES – REPRESENTING THE STRUCTURE OF A DECISION PROBLEM.

6.1          Key concepts and terminology

6.2          Measuring a test’s capability to reveal the patient’s true state

6.3          Constructing the decision tree for a medical decision problem

Epilogue

Bibliography

CHAPTER 7 DECISION TREE ANALYSIS

7.1 Folding-back operation

7.2 Sensitivity analysis

Epilogue

Bibliography

CHAPTER 8  OUTCOME UTILITY – REPRESENTING RISK ATTITUDES

 

8.1 What are risk attitudes?

8.2 Demonstration of risk attitudes in a medical context

8.3  General observations about outcome utilities

8.4 Determining outcome utilities – Underlying concepts

Epilogue

Bibliography

CHAPTER 9  OUTCOME UTILITIES – CLINICAL APPLICATIONS

 

9.1          A parametric model for outcome utilities

9.2          Incorporating risk attitudes into clinical policies

9.3          Helping patients communicate their preferences

 

CHAPTER 10  OUTCOME UTILITIES – ADJUSTING FOR THE QUALITY OF LIFE

10.1   Example – Why the quality of life matters.

10.2   Quality-lifetime tradeoff models

10.3    Quality-survival tradeoff models

10.4   What does it all mean? – An extended example

Epilogue

Bibliography

CHAPTER 11 SURVIVAL MODELS – REPRESENTING UNCERTAINTY ABOUT THE LENGTH OF LIFE

11.1  Survival model basics

11.2   Medical example – Survival after breast cancer recurrence

11.3  Exponential Survival Model

11.4  Actuarial survival models

Epilogue

Bibliography

CHAPTER 12 Markov Models –Structure

12.1 Markov model basics

12.2 Determining transition probabilities

12.3 Markov model analysis – An overview

Epilogue

Bibliography

CHAPTER 13  SELECTION AND INTERPRETATION OF DIAGNOSTIC TESTS

13.1 Four principles of decision making

13.2   The threshold probability for treatment

13.3 Threshold probabilities for testing

13.4   Clinical application of the threshold model of decision making

13.5 Accounting for the disutility of undergoing a test

13.6   Sensitivity analysis

13.7 Decision curves analysis

CHAPTER 14  MEDICAL DECISION ANALYSIS IN PRACTICE: ADVANCED METHODS

11.1        An overview of advanced modeling techniques 

11.2        Use of medical decision-making concepts to analyze a policy problem:

 the cost-effectiveness of screening for HIV         

11.3        Use of medical decision-making concepts to analyze a clinical diagnostic

 problem: strategies to diagnose tumors in the lung         

11.4        Calibration and validation of decision models     

11.5        Use of complex models for individual-patient decision making

 

CHAPTER 15 COST-EFFECTIVENESS ANALYSIS

15.1 The clinician’s conflicting roles: patient advocate, member of society, and entrepreneur

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

15.3 Cost-benefit analysis: a method for measuring the net benefit of medical services

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 analysis