Clinical Reasoning and Differential Diagnosis

Presenter

• Eric Strong from Stanford University and Palo Alto VA Hospital

Video Series Overview

• Title: A Guide to Clinical Reasoning: How to Create an Accurate Differential Diagnosis from a Patient's Presentation
• Three-part series:
  • Part 1: Introduces the clinical reasoning process
  • Part 2: Example for preclinical students
  • Part 3: Example for interns

Learning Objectives

1. Demonstrate a standardized approach to generating a focused differential diagnosis.
2. Create concise problem representations using semantic qualifiers and clinical syndromes.
3. Understand types of frameworks for presentation features.
4. Identify categories of diagnosis for differential diagnosis.

Problems with Current Literature

• Often uses dense terminology.
• Focuses on abstract concepts difficult to apply at the bedside.

Clinical Reasoning Defined

• Definition: Process of interpreting objective data from a patient to make a diagnosis or treatment plan.
• Involves:
  • Interpretation of subjective data
  • Evaluation of data accuracy
  • Synthesis of data
  • Application of biostatistics
  • Integration of knowledge for decision-making

Five Steps to Generating a Differential Diagnosis

1. Acquire Data
   • Use all sources: patient interview, exams, tests, charts, and possibly collateral information.
   • Beware of anchoring bias: reevaluate diagnosis if new data conflicts with initial assessments.
2. Identify Key Features
   • Differentiate diagnosis using individual presentation elements.
   • Includes both positive and negative findings from history, exams, labs, etc.
3. Create a Problem Representation
   • Use semantic qualifiers and clinical syndromes.
   • Semantic Qualifiers: Qualitative abstractions like acute vs. chronic.
   • Clinical Syndromes: Group related findings into a syndrome (e.g., severe sepsis).
   • Construct concise, informative problem representations using medical terminology.
4. Adopt a Framework
   • Framework types: Anatomic, Physiologic, Mnemonic.
   • Example frameworks include categorizing by renal failures or anemia mechanisms.
5. Apply Key Features to the Framework
   • Estimate likelihood of diseases using frameworks.
   • Consider history, exam findings, and diagnostic data.

Differential Diagnosis

• Definition: List of possible diagnoses explaining a patient’s presentation.
• Should include:
  • Most likely diagnosis
  • Common diagnoses
  • "Don’t miss" diagnoses that are rapidly fatal
  • Diagnoses suggested by standout historical features
• Keep it focused (4-6 diagnoses) rather than broad and unhelpfully long.

Important Considerations

• Differentiate between framework and patient-specific differential diagnosis.
• Prioritize likelihood and danger of missed diagnoses.
• Understand that textbook descriptions may not match real-life presentations.

Conclusion

• This concludes Part 1 of the video series.
• Parts 2 and 3 will provide real-world application examples.

---

Key Takeaway

• Use structured, evidence-based approaches to clinical reasoning to minimize bias and maximize diagnostic accuracy.