Context Health professions trainees must acquire a vast amount of clinical knowledge and skills, and a deliberate instructional design approach is needed to provide trainees with effective learning strategies. One powerful yet counterintuitive strategy that facilitates long‐term learning is incorporating intentional difficulties during the learning process. Difficulties that require more effort from learners may impede short‐term learning but are ultimately beneficial for long‐term learning and are therefore termed Desirable Difficulties. Objectives In this cross‐cutting edge paper, we describe the Desirable Difficulty effect from three theoretical perspectives originating in different fields, discuss common evidence‐based Desirable Difficulty strategies used in Health Professions Education and explore emerging research that could further optimise Desirable Difficulty‐enhanced learning for health professions trainees. Methods We synthesise theory and research from psychology, cognitive science and Health Professions Education literatures to further the understanding and application of Desirable Difficulties. We introduce three theoretical perspectives that provide a comprehensive overview of the theoretical underpinnings of the Desirable Difficulty effect: the New Theory of Disuse, the Challenge Point Framework and Cognitive Load Theory. We then illustrate how three common Desirable Difficulty strategies in medical education research—retrieval practice, spaced practice and interleaved practice—can be understood through these theoretical lenses. Finally, we provide relevant examples from the literature and explore emerging research in this area. Conclusions This paper summarises the theory and empirical research on Desirable Difficulties during the learning process, from explaining what they are and why they may be effective to how they have been applied in different contexts. We argue that providing educators and trainees with a comprehensive theoretical and applied understanding of Desirable Difficulty will promote deliberate instructional design decisions and lead to more effective learning.

IntroductionSensitivity, specificity, and predictive values—the basic statistics behind using and interpreting screening and diagnostic tests—are taught in all medical schools, yet studies have shown that a majority of physicians cannot correctly define and apply these concepts. Previous work has not rigorously examined this disconnect and attempted to address it.MethodsWe used adult learning theory to design a case-based interactive workshop to review biostatistics and apply them to clinical decision-making using Bayes' theorem. Participants took an anonymous multiple-choice pretest, posttest, and delayed posttest on definitions and application of the concepts, and we compared the scores between the three tests. Several experiences with early iterations provided feedback to improve the workshop but were not included for analysis.ResultsWe conducted the finalized workshop with 54 pediatrics students, residents, and faculty. All learners completed the immediate pre- and posttests, and eight completed the delayed posttest. Average scores rose from 4.5/8 (56%) on the pretest to 6.5/8 (81%) on the posttest and 6.4/8 (80%) on the delayed posttest. Two-tailed t tests showed p < .001 for the difference between the pretest and both posttests, and post hoc power analysis showed a power of 99% to detect the observed differences. There was no significant difference (p = .8) between the posttest and delayed posttest.DiscussionOur work demonstrates that an interactive workshop reviewing basic biostatistics and teaching rational diagnostic testing using Bayes' theorem can be effective in connecting theoretical knowledge of biostatistics to evidence-based decision-making in real clinical practice.

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