visual diagnostic reasoning: from theory to...
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Visual diagnostic reasoning: from theory to practice
Dr. Amy Warren BSc., BVSc. (hons), PhD, DACVP Associate Professor Pathology
Faculty of Veterinary Medicine, University of Calgary, Canada
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OVERVIEW 1. What is clinical reasoning? 2. Theories of clinical reasoning in
experts 3. Visual diagnostic reasoning
Clinical reasoning is as difficult to define as it is to teach
(Linn et al, 2008, Teaching in General Practice 41:1)
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15 year old quarter horse mare with a history of recurrent laminitis
Polyuria and polydipsia Polyphagic Hyperhidrosis
Laboratory data: Fasting hyperglycemia and
glycosuria Mild anemia Lymphopenia Elevated liver enzymes Elevated serum ACTH
wikimedia
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What is clinical reasoning?
The sum of thinking and decision-making processes associated with clinical practice.
(Higgs, 2008)
Clinical reasoning
Cognition (problem solving)
Context
Knowledge
Metacognition
Tolerance to ambiguity
High stakes
Reflexive understanding
ebollerSticky NoteUNIQUE TO VET MED, ESP WITH FINANCIAL CONSTRAINTS THAT LEAVE HIOLES IN OUR INFORMATION SET
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The nature of clinical reasoning
Problems are ill-structured and evidence is ambiguous Decision making needs to occur based on
dynamic and ill-defined information Shifting and competing goals and
outcomes Occurs on action-feedback loops Decisions have elements of time-
pressure, personal stress and highly significant outcomes for patients
Yet, it is one of the most defining features of expertise and practice excellence.
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Research and understanding clinical reasoning
Complex mostly invisible process Automatic and highly attenuated Highly individualized Requires a range of capabilities:
Cognition, Metacognition, Emotion, Reflexive and Social
ebollerSticky Notehighly exp clinician that recognizes dz X coming from 'gut' but how tdo you explain what went into that thinking/reasoning to a student?
ebollerSticky Noteeg forward, backward, hypothetical deductive, etc
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Signalment and history
Clinical exam findings
Bowen, J. L. (2006) Educational strategies to promote clinical diagnostic reasoning. NEJM. 355:3317-25.
CBC/ Chemistry
Specialized testing (ACTH/ cortisol levels)
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Diagnostic Reasoning Theories (as a cognitive process)
Analytical reasoning Hypothetico-deductive Forward and backward reasoning Bayes Theorem
Non-analytical reasoning Pattern recognition Knowledge structures Script theory
Dual processing theory
ebollerSticky Noteimp in dx testing - using probabilities to test your dx
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Analytical reasoning Hypothetico-deductive reasoning Forward and backward reasoning
strategies Bayes theorem
Pre- and post-test probabilities and likelihood ratios
Errors in frequency estimations: Overestimation of vivid or easily
recalled events/ Underestimation of ordinary events
Overestimation of frequency of events that fit a representative case
Probabilities at the extreme (close to 0 and 1) tend to be over and underestimated
Bowen, J. L. (2006) Educational strategies to promote clinical diagnostic reasoning. NEJM. 355:3317-25.
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Diagnostic Reasoning Theories (as a cognitive process)
Analytical reasoning Hypothetico-deductive Forward and backward reasoning Bayes Theorem
Non-analytical reasoning Pattern recognition Knowledge structures Script theory
Dual processing theory
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Non-analytical reasoning
Pattern recognition (Patel, Norman, Eva, Groen, Schmidt)
Rapid, automatic and non-verbal Experience Automated retrieval knowledge
Knowledge structures Script theory
ebollerSticky Notein the novice can be dangerous , eg students desperately wnat to pattern revognize, eg when you teach a dx, they think everything has that
ebollerSticky Noteusin gcase exemplars assoicated with a knowledge structure
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Knowledge structures
Circulating immune complexes deposit in basement membrane
Complement, humoral and cell-mediated
immune cell damage to glomerulus
Increased leakiness of glomerulus
approximately same MW as albumin
PROTEINURIA
Type III hypersensitivity
Glomerulonephritis
Antibody-antigen
structure and production
Glomerular microanatomy and renal physiology
http://upload.wikimedia.org/wikipedia/commons/d/d8/Dalmatian_black_front.jpgebollerSticky Noteas time goes by, the knowledge drops off and the dz then gets represented by the visual/clinical entity and the things on teh following slide
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Knowledge structures
Causes (Lyme, Babesia, RMSF, Ehrlichia, hepatozoon, Leishmania, CAV-1, Leptospirosis, heart worm, trypanosomiasis, schistosomiasis, GI disease, neoplasia, SLE, hypertension, hyperadrenocortism, juvenile renal dysplasia, congenital glomerulopathy)
Relative prevalence and likelihood of diagnoses (dependent on animals age, breed, location, history, etc.)
Diagnostic tests that differentiate DDx (expected results, normal reference intervals, acceptable ranges for DDx)
Possible treatments Prognosis
Dotty the Dalmatian with proteinuria
Glomerulonephritis
http://upload.wikimedia.org/wikipedia/commons/d/d8/Dalmatian_black_front.jpgebollerSticky Noteyes exp'd clinicians may foget the basics but they become very impo when we get tough cases
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Encapsulation theory Co
ncep
tual
Adapted from Schmidt (1990) Acad Med 65(10):611-621
Stage 1: Development of elaborated causal networks Relatedness of concepts Cause and consequence of pathophysiologic processes
Stage 2: Compilation of elaborated networks into abridged ones
Extensive and repeated application High level simplified causal models using diagnosis to explain signs and symptoms
Stage 3: Emergence of Illness scripts Stage 4: Storing patient encounters as Instance scripts Ex
perie
ntia
l
ebollerSticky Noteas they go into clinical as students
ebollerSticky Noteas they go into careers, building experience, collecting cases
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Script theory (Schmidt 1990, Charlin 2000, Charlin 2007)
Illness scripts that are activated by pattern recognition
Cognitive structures present in experienced clinicians Constructed during repeated experience More efficient task performance
Hierarchical with slots pertaining to: Predisposing conditions Clinical signs Pathophysiology Diagnostic testing Acceptable and not acceptable values Treatment options Prognosis
ebollerSticky Noteyou see this then think about what should be there if it really is PPID
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Analytical reasoning Hypothetico-deductive Bayes theorem Forward reasoning Backward reasoning
Non-analytical reasoning Pattern recognition Automated retrieval of
knowledge structures Script theory
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Diagnostic Reasoning Theories (as a cognitive process)
Analytical reasoning Hypothetico-deductive Forward and backward reasoning Bayes Theorem
Non-analytical reasoning Pattern recognition Knowledge structures Script theory
Dual processing theory
ebollerSticky Notefor years CR was pigeon holed ionto one or the other, eg novice does AR and expert does NAR
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Dual process theory of human judgment (Kahneman 2003, Evans 2008)
Adapted from Evans et al. (2008) Annu Rev Psychol; 59:255-278.
System 1 and System 2 reasoning processes in human decision-making
Non-analytical Analytical
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Dual process theory in clinical reasoning
System 1 default overridden by system 2: Strong deductive reasoning instructions, dissonant patterns,
high cognitive ability System 1 highly related to experience
Repetitive system 2 processing can lead to a system 1 response System 2 impaired by stresses on working memory
Includes lack of sleep, competing thought processes, emotion, time pressure
Clinicians use a mixture of intuition and analysis in decision making
No better system- both are necessary and both are involved in diagnostic error
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Our understanding of clinical reasoning so far..
System 1 and system 2 dual reasoning theories likely accounts for much of the cognitive processes of clinical reasoning Experts with familiar cases tend to use system 1 reasoning Novices tend to use system 2 reasoning Experts are triggered to use system 2 reasoning in unfamiliar cases,
cases with dissonant data or as a checks and balance to their reasoning
Not likely a dichotomy but a continuum with clinicians switching back and forth between system 1 and system 2 reasoning
Experts possess an extensive and multidimensional knowledge base = SCRIPTS Initial exposure of clinicians to a case typically stimulates initial
System 1 retrieval of multiple illness scripts
ebollerSticky Noteeg if it is a legal case
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Veterinary medicine is a visual science Visual reasoning
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FNA Subcutaneous mass, 6 year old, FS, Rottweiler
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Bowen, J. L. (2006) Educational strategies to promote clinical diagnostic reasoning. NEJM. 355:3317-25.
Verbal data (history) Clinical findings (PE) Additional exams or diagnostic tests
Visual Reasoning
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When do we use visual reasoning skills?
Pathology Dermatology Diagnostic imaging
Visual Reasoning
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Sensation and perception
Comparison of image to sensory, short and long term memory
Visual Reasoning
ebollerSticky Notethis process is much more Type 1 CR
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Sensation and perception Entire scene in
peripheral vision interesting
features pop out
Scene disengaged, foveal attention
on area of interest Fixation
Eyes reposition to new area of
interest Saccade
Visual Reasoning
ebollerSticky Noteeyes dart around the image in a particular pattern as described here. Things taht we 'need' to the see to make a diagnosis
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Eye-tracking technology Eye position represents
visual attention Fixations and saccades of
eye-movement
Visual Reasoning
ebollerSticky Notered - fixationgrey - saccades
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Visual reasoning hypothesis
Experts develop sophisticated illness scripts triggered by visual images
Using eye-tracking we expect that experts have more efficient and targeted viewing of a slide, akin to system 1 thinking
Experts differ from novices by: Reduced total time which slides are viewed Select more diagnostically relevant fixation points Spend more time on diagnostically relevant fixation points
Novice-Expert study
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Novice-expert study
Equipment set up
Round cell images 1-5 with AOIs
Dependent variables: 1. Diagnostic accuracy (dichotomous) 2. Percentage time spent viewing the
area of interest 3. Time to diagnosis 4. Eye-movement pattern 5. Talk aloud protocol
Novice-Expert study
ebollerSticky Noteto switch them into system 2 and explain why they came up with the dx
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Warren et al (2016) JVME In Press
What differences do we see between novice and experts? 1. Experts have a much shorter time
to diagnosis
2. Experts spend more time visualizing key features and use more individual key features to make a diagnosis than novices
3. Experts have efficient eye-pattern movements
4. Experts use higher order knowledge collation terms
5. Experts had a higher level of diagnostic accuracy
ebollerSticky Noteexperts left out 'the noise' eg blood/debris in background and were able to pick ou thte key features that are needed for the dx
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What can we conclude?
Experts employ pattern-recognition (System 1) and System 1 reasoning fast, intuitive (quicker to diagnosis) More efficient eye-movements
Experts use script inductive reasoning with analytic (System 2) justification
More diagnostic features identified suggests script induction Suggest experts develop a sophisticated set of visual illness
scripts
Novice-Expert study
ebollerSticky Noteand non-replicable - highly individualised
ebollerSticky Notechecks and balances in system 2 - eg 30% in system 1 then 70% system 2 to make sure they are correct
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Multimodal assessment of visual diagnostic reasoning
Where to next?
In collaboration with Dr. Kent Hecker fMRI showed different levels of activation within novices
between hard and easy clinical reasoning scenarios System 2 reasoning is localized to the prefrontal cortex
neural areas of activation in novice and expert clinicians during clinical reasoning tasks
ebollerSticky Noteexperts use in system 2 in hard cases as do students al lthe time. in easier cases they use thalamic, lower level thinking mechanisms
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This is all great.. but what does all this mean in teaching and learning?
Stay tuned for tomorrow!
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Acknowledgments Collaborators: Dr. Kent Hecker Dr. Tyronne Donnon Dr. Catherine Wagg Dr. Nicole Fernandez Dr. Heather Priest Research technician- Jason Abboud DVM class of 2014 and 2015
Funding Zoetis UCVM veterinary educational
research fund UCVM clinical research fund
Visual diagnostic reasoning: from theory to practiceSlide Number 2Slide Number 3What is clinical reasoning?The nature of clinical reasoningResearch and understanding clinical reasoningSlide Number 7Diagnostic Reasoning Theories(as a cognitive process)Analytical reasoningDiagnostic Reasoning Theories(as a cognitive process)Non-analytical reasoningKnowledge structuresKnowledge structuresEncapsulation theoryScript theory (Schmidt 1990, Charlin 2000, Charlin 2007)Slide Number 16Diagnostic Reasoning Theories(as a cognitive process)Dual process theory of human judgment(Kahneman 2003, Evans 2008)Dual process theory in clinical reasoningOur understanding of clinical reasoning so far..Veterinary medicine is a visual scienceSlide Number 22Slide Number 23When do we use visual reasoning skills?Sensation and perceptionSensation and perceptionEye-tracking technologyVisual reasoning hypothesisNovice-expert studySlide Number 30What can we conclude?Multimodal assessment of visual diagnostic reasoningThis is all great.. but what does all this mean in teaching and learning?Acknowledgments
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