Clinical Reasoning Clinical Reasoning in Differential Diagnosis Experts

Clinical Reasoning

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

Analytic Process Presenting Clinical Features Diagnostic Hypotheses Posterior Probability A Dx 1 Pr (Dx 1) B Dx 2 Pr (Dx 2) C Dx 3 Pr (Dx 3) Elstein, 1978

Non-analytic Process Presenting Clinical Features A Filter through prior episodes A, B, D, F Diagnostic Hypotheses Pr (Dx 1) B C D B, D, G, R C, F, G, H Pr (Dx 2) Pr (Dx 3)

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

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

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

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

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

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

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

Cognitive sciences- based training ¡ Differences l l 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

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

Cognitive sciences- based training ¡ Results l KBIT group diagnosed correctly more test cases than FS group 74. 2% vs 59. 9% (P < 0. 001; effect size = 1. 42)

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.

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