Medical education should teach heuristics rather than train them away

Ample research suggests that most decisions are based on heuristics-simple rules of thumb-that violate prescriptions of logic and probability theory and should therefore be avoided. Yet findings on decision making in everyday work contexts support the idea that heuristics are in fact the very basis of good decision making if adapted to the challenges and performance criteria of the specific work domain. Because traditional pedagogies aim at circumventing heuristics in (clinical) decision making, ways in which to improve the use of heuristics via (medical) education have rarely been explored.

To describe the rationale for teaching and learning proper use of heuristics, rather than stigmatising them, and to identify principles and potential implications for the design and improvement of pedagogies for training in clinical decision making.

Based on theory and evidence concerning heuristic decision making in everyday work domains, we suggest that heuristics should not be avoided as irrational or a mere source of errors, in particular in domains where errors are unavoidable. Instead, we should teach and learn how to use heuristics to make fewer and 'smarter' mistakes rather than 'dumb' ones. Based on concepts borrowed from signal detection and control theory, we demonstrate that, to improve heuristic decision making, teaching should focus on differential diagnoses and learning from feedback and mistakes, in teams and in contextually rich settings where the frequencies, costs and trade-offs between different types of errors (misses versus false alarms) can be experienced. We discuss three possible teaching formats and how to best implement them based on our findings.

The most promising way to train (future) physicians and other health professionals in clinical decision making is not to circumvent heuristics or correct deviations from logic and probability theory but to enhance the use of heuristics by improving perspicacity, that is, by tuning the (recognition) processes that underlie the domain-specific adaptive selection of heuristics and management of ensuing errors.