Biomedical Decision Making: Probabilistic Clinical Reasoning
After reading this chapter, you should know the answers to these questions: How is the concept of probability useful for understanding test results and for making medical decisions that involve uncertainty? How can we characterize the ability of a test to
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Biomedical Decision Making: Probabilistic Clinical Reasoning Douglas K. Owens and Harold C. Sox
After reading this chapter, you should know the answers to these questions: • How is the concept of probability useful for understanding test results and for making medical decisions that involve uncertainty? • How can we characterize the ability of a test to discriminate between disease and health? • What information do we need to interpret test results accurately? • What is expected-value decision making? How can this methodology help us to understand particular medical problems? • What are utilities, and how can we use them to represent patients’ preferences? • What is a sensitivity analysis? How can we use it to examine the robustness of a decision and to identify the important variables in a decision? • What are influence diagrams? How do they differ from decision trees?
D.K. Owens, MD, MS (*) VA Palo Alto Health Care System, Palo Alto, CA, USA Henry J. Kaiser Center for Primary Care and Outcomes Research/Center for Health Policy, Stanford University, Stanford, CA, USA e-mail: [email protected] H.C. Sox, MD, MACP Dartmouth Institute, Geisel School of Medicine at Dartmouth, Dartmouth College, 31 Faraway Lane, West Lebanon, NH 03784, USA
3.1
he Nature of Clinical T Decisions: Uncertainty and the Process of Diagnosis
Because clinical data are imperfect and outcomes of treatment are uncertain, health professionals often are faced with difficult choices. In this chapter, we introduce probabilistic medical reasoning, an approach that can help health care providers to deal with the uncertainty inherent in many medical decisions. Medical decisions are made by a variety of methods; our approach is neither necessary nor appropriate for all decisions. Throughout the chapter, we provide simple clinical examples that illustrate a broad range of problems for which probabilistic medical reasoning does provide valuable insight. As discussed in Chap. 2, medical practice is medical decision making. In this chapter, we look at the process of medical decision making. Together, Chaps. 2 and 3 lay the groundwork for the rest of the book. In the remaining chapters, we discuss ways that computers can help clinicians with the decision-making process, and we emphasize the relationship between information needs and system design and implementation. The material in this chapter is presented in the context of the decisions made by an individual clinician. The concepts, however, are more broadly applicable. Sensitivity and specificity are important parameters of laboratory systems that flag abnormal test results, of patient monitoring systems (Chap. 19), and of information-retrieval systems (Chap. 21). An understanding of what
E.H. Shortliffe, J.J. Cimino (eds.), Biomedical Informatics, DOI 10.1007/978-1-4471-4474-8_3, © Springer-Verlag London 2014
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probability is and of how to adjust probabilities after the acquisition of new information is a foundation for our study of clinical decision-support syste
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