When I was taking my qualifying examinations for my Ph.D. in Psychology, one of my examiners asked me to address what he called the “moon question”: “If human beings are so dumb (according to decision psychology), how did we get to the moon?” The answer, of course, is that despite the predilection in cognitive psychology for inducing and examining error (because error usually provides powerful tests of process models of human behavior), most people are pretty good thinkers most of the time, and some people are very good thinkers most of the time.

Atul Gawande’s excellent 2002 collection Complications: A Surgeon’s Notes on an Imperfect Science struck a responsive chord with medical decision scientists with its insightful examination of medical error. Gawande has now collected 11 new essays in Better: A Surgeon’s Notes on Performance (Metropolitan Books, 2007), which shift the focus to how some physicians come to excel in their craft (in his terms, becoming “positive deviants”). He asks “what does it take to become good at something in which failure is so easy, so effortless?”

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Subjective confidence is usually thought of as the degree to which a person believes they are correct about a judgment and are willing to say so. Confidence can be important when there is no objective guide to accuracy; in these cases, decision makers will usually prefer to make the judgment in which they have the greatest confidence; confidence can drive further behaviors (Weber, Böckenholt et al. 2000). Accordingly, there has been some concern that decision makers have appropriate levels of confidence in their judgments.

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A major problem in all preference or utility assessment, particularly holistic assessments that require the visualization of a health state in its entirety, is that people must often be asked to assess their preferences for health states that they have not yet experienced. That is, they must predict how they will feel about future health states that may arise as a result of their decisions.

Unfortunately, people are generally poor predictors of future experiences for several reasons.

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The key rule for evaluating options that include outcomes that are uncertain is the expectation principle: the value of being exposed to the possibility of an outcome is determined by the value of the outcome and the frequency with which it would be experienced if you were exposed to the possibility repeatedly. For example, facing an one-in-twelve chance of losing a year of life expectancy should be evaluated as facing a certain loss of one month (1/12th of a year) of life expectancy. The expected value of such an option is loss of a month of life.

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One major strategy for managing uncertainty is seeking additional information about the likelihood of outcomes. New information may enable a patient to reduce their uncertainty directly, as when new research studies provide more insight into patient outcomes and suggest increase the likelihood that a particular treatment will or will not be beneficial. Even when new information does not yield greater certainty about outcomes, however, it may serve to narrow the range of the uncertainty.

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Every guide to decision making emphasizes the importance of goals. Decision makers must clarify their goals when making a decision, lest they make choices that will not serve their ends. To consider alternatives without knowing one’s goals is to let the tail wag the dog.

Practically speaking, most medical decision models don’t (and perhaps can’t) consider patient goals directly. Good clinicians, however, must (and do).

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This is the blog for the forthcoming book Making Medical Decisions: A Physician’s Guide by Alan Schwartz, Ph.D., and George Bergus, M.D., which will be published by Cambridge University Press in 2008.

We’ll use this blog to discuss the central concepts of the book and provide physicians with links to tools that help faciliate medical decision making for their patients.