Course Level: Beginner
Format Requirements: The course will consist of presentation of information using lectures and handouts, and individual, small group, and full class activities. Exercises applying the normative principles of diagnosis to patient cases will clarify the relevance of those rules. Computer aids that illustrate the relevant norms will be provided and demonstrated, covering the calculation of treat/test/no-treat threshold probabilities for individual items of diagnostic information, the application of Bayes' theorem to clinical information, and the assessment of potential information for usefulness in reducing diagnostic uncertainty. Cases presented for intuitive diagnosis will demonstrate psychological phenomena that may produce inaccurate diagnoses. There will be ample opportunity to discuss the implications of the material. Requirements include familiarity with basic probability theory and a readiness to explore spreadsheet applications and participate in interdisciplinary discussions.
Background: Diagnosis is the archetypical first step in medical decision making. For over 30 years psychology has compared clinicians' use of diagnostic information to normative models for diagnosis. Prescriptions for optimal strategies and tools to aid diagnosis have been developed. And yet diagnostic errors are still common, and commentators lament a decline in clinical diagnosis skills. We'll review the insights of this work. We'll cover the theory and formulae supporting the prescriptive concepts, and ways to make them more accessible in practice. We'll describe the ways practitioners have commonly been observed to reason diagnostically, and highlight ways this may depart from the norm. We'll identify underutilized concepts and give the opportunity to apply the concepts and reflect on why it is difficult to use them. Participants will also learn about research aimed at training clinicians to use information in normative ways and discuss why these efforts have produced mixed results.
Description and Objectives: The overall aim of this course on the psychology of medical diagnosis is to promote good diagnostic performance, an essential part of medical practice with potentially far-reaching consequences for the well-being of patients and the costs of health care. We'll cover these aspects of the psychology of clinicians’ diagnostic performance:
selection of useful questions regarding the cause of the patient’s complaint,
adjustment of diagnosis probability, and
decision to act on a diagnosis or diagnoses.
Understanding good diagnostic performance requires understanding the nature of the diagnostic task and the best way to use information diagnostically, as well as a vocabulary for describing how clinicians do these tasks and for measuring their shortcomings. Attendees will be shown the normative rules (for categorizing based on symptom diagnosticity, for updating probabilities with Bayes’ theorem, for deriving test thresholds or action thresholds based on the utility of missing a diagnosis or unnecessarily treating for it, and for selecting the most useful information to seek). Methods for characterizing what clinicians do will be demonstrated, and used to illustrate phenomena of clinician diagnosis which may interfere with accurate diagnostic performance.
The learning objectives are to:
1. (knowledge/terminology) – acquire a familiarity with the vocabulary to describe the nature of diagnostic tasks and to measure clinicians' performance on such tasks .
2. (comprehension/interpretation) – develop understanding of the various concepts involved in normative clinical decision making (e.g., Bayesian updating of disease probabilities in light of new information, formulating utility estimates for various decision outcomes, explicating impact of false-positive and false-negative errors, identification of questions likely to reduce uncertainty) through discussion and applications.
3. (comprehension/implications) – comprehend the various psychological factors that influence clinicians' performance on diagnostic tasks (e.g., attention and cognitive effort, experience and expertise, task structure) and the challenges researchers face when studying them.