TEACHING CLINICAL REASONING FOR

STUDENTS, RESIDENTS & FACULTY

Key Points For The “Teachers”

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

Key Points For The “Learner’s”

“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

The Clinical Reasoning Process: How Experts Think Diagnostically

“Expert” Clinicians

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

Teaching Physicians –> Residents & Faculty Should…

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

The Evolution Of A Learner’s Clinical Reasoning Pattern

How Do I Not Look Like An Idiot?

http://www.youtube.com/watch?v=dMAS2S51bM8&feature=player_detailpage

Phase 1: Symptom-Only Diagnosis

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

Phase 2: Symptom-Cluster Pattern Recognition

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

Phase 3: Combining Symptom Clusters With Narrative Stories

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

Phase 4: Analytical & Hypothetical Deductive Reasoning

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

Phase 4: continued

**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

Phase 5: Transition From Novice To Expert

**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}

Heuristics In Clinical Reasoning – The “Non-Analytic Method”

“Heuristics” Definition:

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”}

The 6 Types Associated with Medical Decision making:

Representational Heuristic – Disease Pattern RecognitionAvailability Heuristic – Diagnoses most easily recalledRecency Heuristic – Diagnoses most recently seenDramatic Heuristic – Diagnoses that are DramaticAnchoring Heuristic – Diagnoses of AttachmentPositive Test Heuristic – Diagnoses justified by Positive

Tests

The Bayesian Theory in Clinical Reasoning – “The Analytic Method”

What Is It?

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

How Does It Work?

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

Let Us Look At This Differently!

Have The Disease

Does Not Have The Disease

Diagnostic TestPositive

True Positive{a}

False Positive{b}

a/{a + b}Positive PREDICTIVE VALUE

Diagnostic TestNegative

False Negative{c}

True Negative{d}

d/{c + d}Negative PREDICTIVE VALUE

a/{a + c}SENSITIVITY

d/{b + d}SPECIFICITY

Sensitivity:a/{a+c}Test accuracy (or probabilityof correct classification) among patients with disease

Specificity:d/{b+d}Test accuracy (or probabilityof correct classification) among patients without disease

What We Can Calculate

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.

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

An Example: What is the post-test probability of a Pulmonary Embolism in a patient with s/s consistent with a PE, after a positive CTA of the Chest (sensitivity 90% and specificity 95%) if the patient has a pre-test probability of 28%?

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!!

Another Example: What is the post-test probability of an Acute Myocardial Infarction in a patient with s/s consistent with an AMI, with a positive Troponin-I (sensitivity 90% and specificity 90%) if the patient has a pre-test probability of 30%?

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%

Another Example: Using The Centor Criteria

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."

The Four {4} Centor Criteria

The patients are judged on four criteria, with one point added for each positive criterion:

1.) History of fever2.) Tonsillar exudates3.) Tender anterior cervical adenopathy4.) 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

The Point System Dictates Management

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%)

What Does This All Mean?

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

A Clinic Example =>”To Test or Not To Test? A patient with Acute Tonsillitis presents with all 4 Centor Criteria being positive (pre-test probability of a positive Strep culture being 60%), would you treat this patient if a Rapid Strep Test is ordered (sensitivity 95% and specificity 98%) but the results are negative?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!!

Management Decisions & Treatment Thresholds: After

Bayes’ Theorem

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……

Treatment Thresholds

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 TreatHigh Benefit

Low Risk0% -------------------------------

TreatLow BenefitHigh Risk

------------------------------100%

Treatment Thresholds Continued

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.

Incorporate Clinical Reasoning Into Teaching

Conceptual Learning

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

Active Learning

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

How Do We Accomplish This?

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

Where Are Errors Made in Clinical Reasoning:

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

Where Else Are Errors Made in Clinical Reasoning:

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

Questions or What Are The Muddiest Points?