Clinical Reasoning

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Clinical Reasoning
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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

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Analytic Process

• Presenting
• Clinical Diagnostic Posterior
• Features Hypotheses Probability
• A Dx1 Pr (Dx1)
• B Dx2
  Pr (Dx2)
• C Dx3 Pr
  (Dx3)
• Elstein, 1978

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Non-analytic Process

• Presenting Filter
• Clinical through prior
  Diagnostic
• Features episodes Hypotheses
• Pr (Dx1)
• Pr (Dx2)
• Pr (Dx3)

A,B,D,F
A
B
B,D,G,R
C
D
C,F,G,H
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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
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Implications for Clinical Teachers

• Teach around examples
• Few, complex examples - suboptimal
• Provide many examples
• Represent range of presentations of specific
  conditions

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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

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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

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Implications for Clinical Teachers

• Encourage learners to use both analytical rule
  knowledge and experiential knowledge

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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

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Cognitive sciences- based training

• Similarities
• Common problem
• Identified differentials for problem
• Introduced each case via use of prototype and
  case example

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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

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Cognitive sciences- based training

• Differences
• KBIT - immediate online formative and contrastive
  feedback tailored to each student
• FS - not possible to deliver tailored feedback

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Cognitive sciences- based training

• Results
• KBIT group diagnosed correctly more test cases
  than FS group
• 74.2 vs 59.9
• (P lt 0.001 effect size 1.42)

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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.

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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