Heuristics and biases: selected errors in clinical reasoning

Abstract

Many clinical decisions are made in uncertainty. When the diagnosis is uncertain, the goal is to establish a diagnosis or to treat even if the diagnosis remains unknown. If the diagnosis is known (e.g., breast cancer or prostate cancer) but the treatment is risky and its outcome uncertain, still a choice must be made. In researching the psychology of clinical judgment and decision making, the major strategy is to compare observed clinical judgments and decisions with the normative model established by statistical decision theory. In this framework, the process of diagnosing is conceptualized as using imperfect information to revise opinions; Bayes' theorem is the formal rule for updating a diagnosis as new data are available. Treatment decisions should be made so as to maximize expected value. This essay uses Bayes' theorem and concepts from decision theory to describe and explain some well-documented errors in clinical reasoning. Heuristics and biases are the cognitive factors that produce these errors.