It all make sense: biomedical knowledge, causal connections and memory in the novice diagnostician

  • Nicole N. Woods
  • Lee R. Brooks
  • Geoffrey R. Norman

DOI: 10.1007/s10459-006-9055-x

Cite this article as:
Woods, N.N., Brooks, L.R. & Norman, G.R. Adv in Health Sci Educ (2007) 12: 405. doi:10.1007/s10459-006-9055-x


Although there is consensus among medical educators that students must receive training in the biomedical sciences, little is known regarding the role of biomedical knowledge in diagnosis. The present paper presents two studies examining the role of biomedical knowledge, specifically knowledge of causal mechanisms, in novice diagnosticians. In Experiment 1, two groups of participants are taught to diagnose a series of artificial diseases. In the causal learning condition students learn the underlying causal mechanisms for each feature. A second group learns the same diseases without the causal explanations. Participants are asked to diagnose a series of written cases immediately after training and again 1 week later. The results show that students who learn a causal model are better able to retain their diagnostic performance over time (89% correct vs. 78%). This finding is investigated further in Experiment 2, demonstrating that students rely more on casual information after a delay (mean probability of 57% vs. 43%). Together, the studies suggest that knowledge of underlying causal mechanisms can aid student memory for diagnostic categories and that use of causal knowledge changes over time.


Basic science Causal knowledge Clinical reasoning Memory Undergraduate medical education Concept formation 

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  • Nicole N. Woods
    • 1
  • Lee R. Brooks
    • 2
  • Geoffrey R. Norman
    • 3
  1. 1.Department of Surgery, The Wilson Centre University of TorontoTorontoCanada
  2. 2.Department of Psychology, Behaviour and NeuroscienceMcMaster UniversityHamiltonCanada
  3. 3.Department of Clinical Epidemiology and BiostatisticsMcMaster UniversityHamiltonCanada