2. Opportunity to teach Learners Clinical Medicine & Clinical Reasoning =>
* Direct Patient Care Observation
*Evaluation of Admission Notes & Discharge Summaries
*Oral Case Presentations on Rounds
Clinical Reasoning Skills For The Learners =>
*Develop through Multiple Phases
*From focusing on Symptoms only, thru the Analytical and
Non-Analytical approaches towards Diagnosis &Treatment
“Expert Clinicians” Use =>
*Intuitive Reasoning
*Pattern Recognition
*Non-Analytical Reasoning
*Analytical Reasoning

3. “Intuitive Clinical Reasoning” for the Novice Physician =>
*Leads to a High Potential for Errors
*”Common Heuristics” or Diagnostic Biases also High Potential for Errors
Principles of Clinical Epidemiology =>
*Instrumental for the Development of a Proper Differential Diagnosis
*Understanding Thresholds for Treatment
Bayesian Theory =>
*Teaches Analytical Clinical Reasoning in an Orderly, Concrete Fashion

5. Use Analytical Reasoning & Non-Analytical, Pattern Recognition
Simultaneously
Use Analytical Reasoning when no clear clinical diagnosis emerges
from the clinical data
Use Analytical Reasoning & Non-Analytical, Pattern Recognition
when appropriate to double-check that the reasoning applied in a
specific case makes sense

6. Highlight “Illness Scripts” =>
key aspects of disease presentations
easily acquired knowledge
**Non-Analytic Component to Teaching
Use Clinical Knowledge
Focused Diagnostic Testing
Bayesian Reasoning =>
**Analytic Component to Teaching
**Best to stress Analytic Component
**Analytical Reasoning is NOT Intuitive
**ONLY develops with Active Teaching

8. http://www.youtube.com/watch?v=dMAS2S51b
M8&feature=player_detailpage

9. Students focus on one symptom
**have difficulty seeing any unifying patterns of a specific
disease amidst a myriad of signs and symptoms
Students do not link signs and symptoms together
**focus on each sign or symptom individually

10. Gaining Clinical Experience => “Encapsulating Knowledge”
**Learner’s Merge Disease Details into “Syndromes”
**Works for Common, Uncomplicated Clinical Presentations
only!
**Learner’s will Regress to “Pathophysiologic, Biomedical
Knowledge” if they do not recognize an obvious
clinical syndrome

11. The Learner’s Knowledge is further Encapsulated into “Illness Scripts”
**Included in these “Illness Scripts” is the knowledge of
predisposing conditions
**Allows the Learner to rapidly exclude categories of diseases
**Pattern recognition is becoming a more prominent aspect of
clinical reasoning

12. Learners start to use Analytical Deduction
**The First Step is developing a Hypothesis {DDx}, with
focused gathering of additional data to confirm or
eliminate the Hypothesis {DDx}
**Diagnostic Testing becomes more focused
**Diagnostic Verification occurs -- Physicians assess the
Adequacy and Coherency of their Diagnoses
=>An Adequate Diagnosis explains all the clinical findings
=>A Coherent Diagnosis explains the Pathophysiology

13. **After completing these steps, Physicians choose a working diagnosis
**Despite remaining uncertain to some degree, they begin to manage the
patient’s illness
**Without clinical instruction, these Analytical and Diagnostic Reasoning
Skills WILL remain rudimentary

14. **Experienced Physicians use the Non-Analytic Method of
Diagnosis very commonly; but also Analytic
Reasoning as commonly
**Teaching Physicians must encourage Learners to use Analytic
Reasoning
**Teaching Physicians must role-model Analytic Reasoning =>
make Learners use Deductive Reasoning {which of the
top three differential diagnoses is most correct relative
to the others}

16. The Intuitive Non-Analytical mental short-cuts used to recognize and
categorize certain illnesses
“Heuristics” are really “Biases”
**can lead to inaccurate diagnoses
**Learner’s need to verify these diagnoses with Analytical
Reasoning {“The Bayesian Theorem”}

17. Representational Heuristic – Disease Pattern Recognition
Availability Heuristic – Diagnoses most easily recalled
Recency Heuristic – Diagnoses most recently seen
Dramatic Heuristic – Diagnoses that are Dramatic
Anchoring Heuristic – Diagnoses of Attachment
Positive Test Heuristic – Diagnoses justified by Positive Tests

19. The Bayes’ Theorem states that Pre-Test Probability of a Diagnosis
DIRECTLY affects the Post-Test Probability of that Disease
**Mathematical Numbers replace Physician’s Gestalt
**Using Patient Data, Prevalence Data & Narrative Patterns for
Diseases, Pre-Test Probabilities are assigned to each Diagnosis
**Using Sensitivity, Specificity, and Likelihood Ratios, the
Physician determines Post-Test Probabilities for a Clinical
Diagnosis

20. Step 1: Assign Numbers for Pre-Test Probabilities – % likelihood of
the Learner’s own Differential Diagnoses
Step 2: Convert Pre-Test Probabilities to Pre-Test Odds - % of the
Learner’s #1 Diagnosis divided by the total % of the remaining
Diagnoses
Step 3: Calculate the Positive Likelihood Ratio for Positive Test
Results. Calculate Negative Likelihood Ratio for Negative Test
Results
Step 4: Post-Test Odds of a Disease Equals The Pre-Test Odds of the
Disease multiplied by the Likelihood Ratio of the Diagnostic Test
Chosen
Step 5: Finally, Calculate The Post-Test Probability by Converting The
Post-Test Odds of the Disease

21. Have The
Disease
Does Not
Have The
Disease
Diagnostic
Test
Positive
True Positive
{a}
False Positive
{b}
a/{a + b}
Positive
PREDICTIVE
VALUE
Diagnostic
Test
Negative
False Negative
{c}
True Negative
{d}
d/{c + d}
Negative
PREDICTIVE
VALUE
a/{a + c}
SENSITIVITY
d/{b + d}
SPECIFICITY

22. Sensitivity:
a/{a+c}
Test accuracy (or probability
of correct classification) among
patients with disease
Specificity:
d/{b+d}
Test accuracy (or probability
of correct classification) among
patients without disease
What We Can
Calculate

23. We Can Now Calculate
Likelihood ratios:
LR+ = Sensitivity / (1 - Specificity)
True Positive/False Positive
LR- = (1 - Sensitivity) / Specificity
False Negative/True Negative
**A likelihood ratio of greater than 1 indicates the test result is
associated with the disease. A likelihood ratio less than 1 indicates
that the result is associated with absence of the disease.

24. Now For The Clinical
Implications!
Pre-Test Odds =
Pre-Test Probability/1 - Pre-Test Probability
Positive Likelihood Ratio =
LR+ = Sensitivity / (1 - Specificity)
Post-Test Odds =
Pre-Test Odds x LR {Bayes’ Theorem}
Post-Test Probability =
Post-Test Odds/1 + Post-Test Odds

25. Calculate Pre-Test Odds: 0.28/1 – 0.28 = 0.389
Calculate Positive Likelihood Ratio: 0.90/1 – 0.95 = 18
Calculate Bayes’ Theorem: 0.389 x 18 = 7.0
Convert Post-Test Odds to Post-Test Probability: 7/1 + 7
= 87.5%
****In a patient with chest pain and SOB, with a positive CTA Chest, the
diagnosis will not 100% of the time be consistent with a Pulmonary
Embolism. So if the patient does not respond to the appropriate
treatment; RE-THINK your Differential Diagnosis!!

26. Calculate Pre-Test Odds: 0.30/1 – 0.30 = 0.429
Calculate Positive Likelihood Ratio: 0.90/1 – 0.90 = 9
Calculate Bayes’ Theorem: 0.429 x 9 = 3.86
Convert Post-Test Odds to Post-Test Probability:
3.86/1 + 3.86 = 79.4%

27. The Centor Criteria are a set of criteria which may be
used to identify the likelihood of a bacterial infection in
patients complaining of a sore throat. They were
developed as a method to quickly diagnose the
presence of a Group A Streptococcal Infection, or a
diagnosis of Streptococcal Pharyngitis in "adult
patients who presented to an urban emergency room
complaining of a sore throat."

28. The patients are judged on four criteria, with one point
added for each positive criterion:
1.) History of fever
2.) Tonsillar exudates
3.) Tender anterior cervical adenopathy
4.) Absence of cough
The Modified Centor Criteria:
5.) Add the patient's age to the criteria:
*Age <15 -- add 1 point
*Age >44 -- subtract 1 point

29. Guidelines for management state:
**<2 points - No antibiotic or throat culture necessary
(Risk of strep. infection <10%)
**2-3 points - Should receive a throat culture and treat
with an antibiotic if culture is positive (Risk of strep.
infection 32% if 3 criteria, 15% if 2)
**>3 points - Treat empirically with an antibiotic (Risk
of strep. infection 56%)

30. The presence of all four variables indicates a 40% to
60% positive predictive value for a throat culture to test
positive for Group A Streptococcus bacteria
The absence of all four variables indicates a negative
predictive value of greater than 80%
**The high negative predictive value suggests that
the Centor Criteria more effectively rules out strep
throat than diagnoses strep throat

31. Calculate Pre-Test Odds: 0.60/1 – 0.60 = 1.5
Calculate Negative Likelihood Ratio: 1 -0.95/0.98 = 0.05
Calculate Bayes’ Theorem: 1.5 x 0.05 = 0.075
Convert Post-Test Odds to Post-Test Probability: 0.075/1 + 0.075 = 0.07%
**A Post-Test Probability of 0.07% suggests that the patient does not
have Strep Pharyngitis. All of us would treat this patient regardless
because of the Centor Criteria; SO IT IS NOT NECESSARY TO
PERFORM A RAPID STREP TEST TO VERIFY THAT THE
PATIENT HAS STREP THROAT….IT WILL NOT CHANGE HOW
YOU MANAGE THE PATIENT, ONLY CONFUSE THE
SITUATION!!

32. The purpose of using Bayes’ Theorem was to establish the
probability of each diagnosis being considered. More
than one diagnosis may in fact still be important……
”Occam’s Razor” One Disease explains All Symptoms
”Hickam’s Dictum” A patient can have more than one
disease
More Testing or Begin Treatment? The Decision to Treat
depends directly on the Physician’s Diagnostic
Confidence……

33. Management decisions are based upon a combination of potential
costs & risks vs. benefits of treatment……..the combination of the
two defines the Treatment Threshold.
Low Risk/High Benefit Treatments {Antibiotic use in Pneumonia} =>
Very Low Treatment Thresholds
High Risk/Low Benefit Treatments {Chemotherapy for Cancer} =>
Very High Treatment Thresholds
Don’t Treat
High Benefit
Low Risk
0% -------------------------------
Treat
Low Benefit
High Risk
------------------------------100%

34. When the Pre-Test Probability for a Disease crosses the Treatment
Threshold
RIGHT => TREAT {exceeds treatment threshold}
LEFT => DO NOT TREAT {fails to reach treatment threshold}
This method exposes important principles of decision making and
helps the clinician further develop a rational, quantitative
approach to the use of diagnostic tests, in addition to using
Bayes Theorem.

36. Involves acquiring knowledge of concepts, factual
descriptions and theoretical constructs. It often occurs
in response to incentives {like evaluations}, and not
because of a perceived benefit to want to learn, or to
use that learning to behave differently.
This makes you a DOCTOR

37. Involves the Learner engaging in activities/tasks,
thinking about what they are doing. The tasks may
range from those that are simple and less structured to
those that are complex and longer in duration. The
tasks are carefully planned and structured. The tasks
are linked to higher order thinking outcomes, and to
explanations about why you are learning and how you
learn.
This makes you a PHYSICIAN

38. Teaching Physicians are striving for “ACTIVE LEARNING.”
Learners need to strive for the same
The Learner must commit to a Diagnosis. Use Bayesian Numbers to
Solidify the Diagnosis
Challenge the Student and the Resident to defend their management
decisions by giving them a contradictory diagnostic opinion

39. Overestimation of a Pre-Test Probability for a Wrong Diagnosis
Underestimation of a Pre-Test Probability for a Correct Diagnosis
Overestimation of the Power of a Diagnostic Test whose results
were positive for the Correct Diagnosis
Underestimation of the Power of a Diagnostic Test whose results
were positive for the Correct Diagnosis

40. Overestimation of the Power of a Diagnostic Test whose results were
negative for the Wrong Diagnosis
Underestimation of the Power of a Diagnostic Test whose results were
negative for the Wrong Diagnosis
Treatment Threshold set too high as the Risk-Benefit ratio
miscalculated
Treating illness prematurely because you set the Treatment Threshold
too Low