Clinical Reasoning Clinical Reasoning in Differential Diagnosis Experts

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Clinical Reasoning

Clinical Reasoning

Clinical Reasoning in Differential Diagnosis Experts use 3 main methods or a combination: v

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

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

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

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

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

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

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

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

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

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

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

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

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

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 ¡

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