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Clinical Reasoning

Clinical Reasoning. Clinical Reasoning in Differential Diagnosis. Experts use 3 main methods or a combination: Analytic or Hypothetico-deductive Non-analytic or Pattern recognition Pathognomonic signs and symptoms. Analytic Process. Presenting Clinical Diagnostic Posterior

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Clinical Reasoning

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  1. Clinical Reasoning

  2. Clinical Reasoning in Differential Diagnosis Experts use 3 main methods or a combination: • Analytic or Hypothetico-deductive • Non-analytic or Pattern recognition • Pathognomonic signs and symptoms

  3. Analytic Process Presenting Clinical Diagnostic Posterior FeaturesHypothesesProbability A Dx1 Pr (Dx1) B Dx2 Pr (Dx2) C Dx3 Pr (Dx3) Elstein, 1978

  4. Non-analytic Process Presenting Filter Clinical through prior Diagnostic FeaturesepisodesHypotheses Pr (Dx1) Pr (Dx2) Pr (Dx3) A,B,D,F A B B,D,G,R C D C,F,G,H

  5. Combined Modelof Clinical Reasoning Both analytic and non-analytic processes combined Eva et al.,2002 Hypotheses Tested Patient Presents Case Representation Non-analytic Interactive Analytic

  6. Implications for Clinical Teachers • Teach around examples • Few, complex examples - suboptimal • Provide many examples • Represent range of presentations of specific conditions

  7. Implications for Clinical Teachers • Practice with cases should mimic eventual use of knowledge • Working through textbook cases is NOT enough • Mixed practice with multiple categories mixed together

  8. Implications for Clinical Teachers • Do NOT rely on students to make comparisons across problems spontaneously • Allow students to identify similarities in underlying concepts of distinct problems • Relate principles in new examples with those in past examples • Provide learners with an opportunity to reveal idiosyncratic mistakes

  9. Implications for Clinical Teachers Encourage learners to use both analytical rule knowledge and experiential knowledge

  10. Cognitive sciences- based training • Research study • 2 different methods for training 2nd year medical students • Traditional classroom based lecture • Cognitive sciences-based approach (KBIT) Papa et al. 2007

  11. Cognitive sciences- based training • Similarities • Common problem • Identified differentials for problem • Introduced each case via use of prototype and case example

  12. Cognitive sciences- based training • Differences • KBIT group - 4 example cases per disease • FS group - 1 case example per disease • KBIT group - actively required to apply knowledge base towards diagnosis of practice cases (35) • FS group - 4-5 cases, with no control over students’ active engagement in the cases

  13. Cognitive sciences- based training • Differences • KBIT - immediate online formative and contrastive feedback tailored to each student • FS - not possible to deliver tailored feedback

  14. Cognitive sciences- based training • Results • KBIT group diagnosed correctly more test cases than FS group 74.2% vs 59.9% (P < 0.001; effect size = 1.42)

  15. Cognitive Biases • Representativeness heuristic - overestimating similarity between people and events • Availability heuristic - too much weight to easily available info • Overconfidence • Confirmatory bias - bias toward positive and confirming evidence • Illusory correlation - perceiving two events as causally related when there is none • Putting initial probability at too extreme a figure and not adjusting for subsequent info Klein, 2005.

  16. Summary • Expertise is not a matter of acquiring a general, all-inclusive reasoning strategy • No one kind of knowledge counts more than any other • Expertise in medicine derives from both formal and experiential knowledge Norman, 2007

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