Advances in Health Sciences Education

, Volume 17, Issue 1, pp 65–79

The impact of selected contextual factors on experts’ clinical reasoning performance (does context impact clinical reasoning performance in experts?)

  • Steven J. Durning
  • Anthony R. Artino
  • John R. Boulet
  • Kevin Dorrance
  • Cees van der Vleuten
  • Lambert Schuwirth
Article

Abstract

Context specificity, or the variation in a participant’s performance from one case, or situation, to the next, is a recognized problem in medical education. However, studies have not explored the potential reasons for context specificity in experts using the lens of situated cognition and cognitive load theories (CLT). Using these theories, we explored the influence of selected contextual factors on clinical reasoning performance in internal medicine experts. We constructed and validated a series of videotapes portraying different chief complaints for three common diagnoses seen in internal medicine. Using the situated cognition framework, we modified selected contextual factors—patient, encounter, and/or physician—in each videotape. Following each videotape, participants completed a post-encounter form (PEF) and a think-aloud protocol. A survey estimating recent exposure from their practice to the correct videotape diagnoses was also completed. The time given to complete the PEF was randomly varied with each videotape. Qualitative utterances from the think-aloud procedure were converted to numeric measures of cognitive load. Survey and cognitive load measures were correlated with PEF performance. Pearson correlations were used to assess relations between the independent variables (cognitive load, survey of experience, contextual factors modified) and PEF performance. To further explore context specificity, analysis of covariance (ANCOVA) was used to assess differences in PEF scores, by diagnosis, after controlling for time. Low correlations between PEF sections, both across diagnoses and within each diagnosis, were observed (r values ranged from −.63 to .60). Limiting the time to complete the PEF impacted PEF performance (r = .2 to .4). Context specificity was further substantiated by demonstrating significant differences on most PEF section scores with a diagnosis (ANCOVA). Cognitive load measures were negatively correlated with PEF scores. The presence of selected contextual factors appeared to influence diagnostic more than therapeutic reasoning (r = − .2 to −.38). Contextual factors appear to impact expert physician performance. The impact observed is consistent with situated cognition and CLT’s predictions. These findings have potential implications for educational theory and clinical practice.

Keywords

Clinical reasoning Medical education Context specificity Expertise Quantitative methods 

References

  1. Anderson, M. L. (2003). Embodied cognition: A field guide. Artificial Intelligence, 149, 91–130.CrossRefGoogle Scholar
  2. Baddeley, A. D. (1986). Working memory. Oxford: Clarendon.Google Scholar
  3. Baddeley, A. D. (1992). Working Memory. Science, 255(5044), 556–559.CrossRefGoogle Scholar
  4. Bordage, G. (1994). Elaborated knowledge: a key to successful diagnostic thinking. Academic Medicine, 69, 883–885.CrossRefGoogle Scholar
  5. Bordage, G., & Lemieux, M. (1991). Semantic structures and diagnostic thinking of experts and novices. Academic Medicine, 65, S70–S72.CrossRefGoogle Scholar
  6. Clauser, B. E., Balog, K., Harik, P., Mee, J., & Kahramen, N. (2009). A multivariate generalizability analysis of history-taking and physical examination scores from the USMLE step 2 clinical skills examination. Academic Medicine, 84, S86–S89.CrossRefGoogle Scholar
  7. Durning, S. J., Artino, A. R., Pangaro, L. N., van der Vleuten, C., & Schuwirth, L. (2010a). Redefining context in the clinical encounter: Implications for research and training in medical education. Academic Medicine, 85, 894–901.CrossRefGoogle Scholar
  8. Durning, S. J., Artino, A. R., Boulet, J., LaRochelle, J., van der Vleuten, C. P. M., & Schuwirth, L. (2011a). The feasibility, reliability, and validity of a post-encounter form (PEF) for evaluating clinical reasoning. Medical Teacher (accepted).Google Scholar
  9. Durning, S. J., Artino, A. R., Boulet, J., van der Vleuten, C. P. M., LaRochelle, J., Arze, B., et al. (2010b). Making use of contrasting participant views of the same encounter. Medical Education, 44, 953–961.CrossRefGoogle Scholar
  10. Durning, S. J., Artino, A. R., van der Vleuten, C. P. M., Pangaro, L., & Schuwirth, L. (2011b). Context and clinical reasoning: understanding the situation from the perspective of the expert’s voice. Medical Education (accepted).Google Scholar
  11. Durning, S. J., LaRochelle, J., Pangaro, L. N., Artino, A. R., Boulet, J., van der Vleuten, C. P. M., & Schuwirth, L. (2011c). Does the authenticity of preclinical teaching format affect clinical clerkship outcomes: A prospective randomized crossover trial. Teaching and Learning in Medicine (in review).Google Scholar
  12. Elstein, A. S., Shulman, L. S., & Sprafka, S. A. (1990). Medical problem solving: A ten-year retrospective study. Evaluation & the Health Professions, 13, 5–36.CrossRefGoogle Scholar
  13. Ericsson, K. A., Charness, N., Feltovich, P., & Hoffman, R. R. (Eds.). (2006). The Cambridge handbook of expertise and expert performance. New York, NY: Cambridge University Press.Google Scholar
  14. Eva, K. W. (2011). On the relationship between problem-solving skills and professional practice. In Elaborating professionalism innovation and change in professional education (pp. 17–34). London: Springer.Google Scholar
  15. Eva, K. A., Neville, A. J., & Norman, G. R. (1998). Exploring the etiology of content specificity: Factors influencing analogic transfer and problem solving. Academic Medicine, 73, S1–S5.CrossRefGoogle Scholar
  16. La Rochelle, J., Durning, S. J., Pangaro, L. N., Artino, A. R., Boulet, J., van der Vleuten, C. P. M., Schuwirth, L. (2011). The effect of increasing authenticity of instructional format on student performance: A prospective randomized trial. Medical Education (accepted).Google Scholar
  17. Lindstom, A., Villing, J., Larsson, S., Seward, A., Aberg, N., & Holtelius, C. (2008). The effect of cognitive load on disfluencies during in-vehicle spoken dialogue. In Proceedings of interspeech.Google Scholar
  18. Monajemi, A., Rikers, R. M. J. P., & Schmidt, H. G. (2007). Clinical case processing: A diagnostic versus a management focus. Medical Education, 41, 1166–1172.CrossRefGoogle Scholar
  19. Norman, G. R., Tugwell, P., Feightner, J. W., Muzzin, L. G., & Jacoby, L. L. (1995). Knowledge and clinical problem solving. Medical Education, 19, 344–356.CrossRefGoogle Scholar
  20. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.CrossRefGoogle Scholar
  21. Pangaro, L. (1999). A new vocabulary and other innovations for improving descriptive in-training evaluations. Academic Medicine, 74(11), 1203–1207.CrossRefGoogle Scholar
  22. Plass, J. L., Moreno, R., & Brunken, R. (2010). Cognitive load theory. New York, NY: Cambridge University Press.Google Scholar
  23. van Merrienboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147–177.CrossRefGoogle Scholar
  24. van Merrienboer, J. J. G., & Sweller, J. (2010). Cognitive load theory in health professional education: Design principles and strategies. Medical Education, 44, 85–93.CrossRefGoogle Scholar
  25. Villing, J. (2009). Dialogue behavior under high cognitive load. In Proceedings of SIGDIAL 2009: The 10th annual meeting of the special interest group in discourse and dialogue, pp. 322–325.Google Scholar

Copyright information

© US Government 2011

Authors and Affiliations

  • Steven J. Durning
    • 1
  • Anthony R. Artino
    • 1
  • John R. Boulet
    • 2
  • Kevin Dorrance
    • 1
  • Cees van der Vleuten
    • 3
  • Lambert Schuwirth
    • 3
  1. 1.Department of Medicine (NEP)Uniformed Services University of the Health Sciences (USU)BethesdaUSA
  2. 2.Foundation for the Advancement of International Medical Education and Research (FAIMER)PhiladelphiaUSA
  3. 3.Maastricht UniversityMaastrichtThe Netherlands

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