Heuristic decision making in medicine

Abstract

Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.

Keywords: biases; bounded rationality; decision aids; ecological rationality; fast-and-frugal heuristics; medical decision making.

Figures

Figure 1.. A simple heuristic for deciding whether a patient should be assigned to the coronary care unit or to a regular nursing bed. If there is a certain anomaly in the electrocardiogram (the so-called ST segment) the patient is immediately sent to the coronary care unit. Otherwise a second predictor is considered, namely whether the patient's chief complaint is chest pain. If not, a third question is asked. This third question is a composite one: whether any of five other predictors is present. This type of heuristic is also called a fast-and-frugal tree. Fast-and-frugal trees assume that decision makers follow a series of sequential steps prior to reaching a decision. Abbreviations: NTG, nitroglycerin; Ml, myocardial infarction; T, T-waves with peaking or inversion. Adapted from ref 58 (based on Green and Mehr)1: Gigerenzer G. Gut feelings: the intelligence of the Unconscious. New York, NY: Viking Press; 2007. Copyright © Viking Press 2007

Figure 2.. The performance of a decision tree for coronary care unit allocations, compared with that of the Heart Disease Predictive Instrument, and physicians' judgments. The x-axis represents the proportion of patients who were incorrectly assigned to the coronary care unit (false positive rate), and the y-axis shows the proportion of patients who were correctly assigned to the coronary care unit (sensitivity). The diagonal line represents chance level, the area to the left of the diagonal better-than-chance. Note that the Heart Disease Predictive Instrument's allocation decisions depend on how sensitivity is traded off against the false-positive rate. This is why several data points are shown for this instrument. Adapted from ref 58 (based on Green and Mehr)': Gigerenzer G. Gut feelings: the Intelligence of the Unconscious. New York, NY: Viking Press; 2007. Copyright © Viking Press 2007

Figure 3.. A simple tree to represent probabilities as natural frequencies, designed to help pyhsicians and patients understand health statistics.

Figure 4.. A fast-and-frugal tree for making decisions about macrolide prescriptions, proposed by Fisher et al (see also Katsikopoulos et al. for an in-depth discussion). Macrolides are the first-line antibiotic treatment of community-acquired pneumonia. The fast-and-frugal tree signals that first-line macrolide treatment may be limited to individuals with community-acquired pneumonia who have had fever for more than 2 days and who are older than 3 years.

Figure 5.. Illustration of how two models fit past observations (filled circles) and how they predict new obsen/ations (triangles). The complex Model A (thin line) overfits the past observations and is not as accurate in predicting the new observations as the simple Model B (thick line Adapted from ref 60: Pitt MA, Myung IJ, Zhang S. Toward a method for selecting among computational models for cognition. Psychol Rev. 2002;109:472-491. Copyright © American Psychological Association 2002