Diagnostic Decision-Making: How do we do it and how can we (and our learners) improve?

1. Diagnostic Decision-Making: How do we do it and how can we (and our learners) improve? META Scholars September 5, 2013

2. Agenda • Overview of diagnostic reasoning • How good are we? • How can we (and our learners) improve?

3. Objectives • Be able to describe the basic process of making a diagnosis • Acknowledge we struggle with making diagnoses • List several ways we can improve our diagnostic skills

4. Overview of Clinical Reasoning • Overview of making a diagnosis • How our brains deal with it • What it actually looks like in practice

5. How do Doctors Think?

6. Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts

7. Data collection • History • Physical examination • Laboratory studies • Imaging studies

8. Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts

9. Problem Representation • Making sense of the data obtained • Identification of the key elements • Categorization • Semantic qualifiers • Frame things (context is everything)

11. Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts

12. Illness Scripts • Mental representations of the key elements of specific diagnoses • History • Physical • Labs • Imaging • Response to therapy

14. Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts

15. Illness Script Selection • Match the problem formulation to the illness script

16. Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts

17. Overview of Clinical Reasoning • Overview of making a diagnosis • How our brains deal with it • What it actually looks like in practice

18. How do doctors think? • We’re not really sure, but we do have a general idea • A couple of key points: • Experience really matters • Lots of complexity

19. Question 1: Image from Wikimedia Commons

21. Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts

22. Question 2:

24. Data Collection Problem Representation (Framing) Potential Match Diagnosis! Access Illness Scripts

25. Overview of Clinical Reasoning • Overview of making a diagnosis • How our brains deal with it • What it actually looks like in practice

26. How it plays out…. • Bedside Clinical Reasoning • Hypothesis generation • Hypotheses refinement • Diagnostic testing • Causal reasoning • Diagnostic verification

27. A Case • 69 year-old man with a history of CAD presents with chest pain • Acute coronary syndrome! • Unlike prior MI • Pain is sharp and stabbing • Less likely ACS, maybe PE? • Pericarditis? • No associated dyspnea • Radiates through to the back • ?Aortic Dissection Hypothesis Generation Hypothesis Refinement and Generation

28. Exam • Differential pulses in upper extremities • Aortic insufficiency murmur • CXR • Widened mediastinum • CT scan • Aortic dissection Causal Reasoning Hypothesis Refinement Diagnostic Testing and Verification

29. Bedside Clinical Reasoning • Hypothesis generation • Hypotheses refinement • Diagnostic testing • Causal reasoning • Diagnostic verification

30. Agenda • Overview of diagnostic reasoning • How good are we? • How can we (and our learners) improve?

31. Definition of a Diagnostic Error: • A diagnosis that, on the basis of the eventual appreciation of more definitive information, was • Unintentionally delayed, or • Wrong, or • Missed altogether

32. Question 3 What is your personal rate of diagnostic error? • <1% • 2-3% • 5% • 10-15% • >20%

33. Question 4 What is the overall rate of diagnostic error in medicine? • <1% • 2-3% • 5% • 10-15% • >20%

34. Rate of Diagnostic Error • Overall, likely rate of diagnostic error is about 10% • Error rate varies by specialty and study • Anatomic pathology 2-5% • ED up to 12% • Medical inpatient diagnosis ~6-8%

35. Do these errors matter? • Account for up to 17% of adverse events • 40,000-80,000 US hospital deaths per year attributable to diagnostic error • 5% of all autopsies show a lethal diagnosis that could have been treated ante-mortem • Tort claims data (really expensive) JAMA 2002; 288:2405

36. What do these errors look like?

37. What causes these errors? • Three general categories of diagnostic error • “No Fault” (7%) • Very unusual presentations, patient-related error • Systems-related (19%) • Technical failure, organizational issues • Cognitive errors (28%) • Faults in knowledge, data gathering, information processing or metacognition 46%

38. Arch Intern Med 2005;165:1493-1499.

39. Basis of Cognitive Errors • Cognitive Errors • Faulty knowledge • Faulty data gathering • Faulty synthesis • Affective error

40. Basis of Cognitive Errors • Cognitive Errors • Faulty knowledge • Faulty data gathering • Failure to ask or look • EMRs • Faulty synthesis • Affective error

41. Red Flag Medicine • We often embrace “Red Flag Medicine” • Overly trusting of technology • Doubt the utility of the clinical exam • Lack confidence in clinical skills !

42. Basis of Cognitive Errors • Cognitive Errors • Faulty knowledge • Faulty data gathering • Failure to ask or look • EMRs • Faulty synthesis • Affective error

43. Basis of Cognitive Errors • Cognitive Errors • Faulty knowledge • Faulty data gathering • Failure to ask or look • EMRs • Faulty synthesis/metacognition • Premature closure • Misjudging the importance of a finding • Faulty context generation

44. Question 5: • List two things that annoy you about people • List three of your favorite people

45. Basis of Cognitive Errors • Cognitive Errors • Faulty knowledge • Faulty data gathering • Faulty synthesis • Affective error

46. Agenda • Overview of diagnostic reasoning • How good are we? • How can we (and our learners) improve?

47. Potential Solutions • Monitoring and feedback systems • Reframe root cause analysis • Provide improved clinical decision support • Mandate/encourage appropriate use of EMRs • Data visualization tools • Cognitive awareness and techniques

48. Expert Performance Experienced Non Expert Time Slide from Gurpreet Dhaliwal

49. Making Experts • Progressive Problem Solving • Feedback • Simulation • Deliberate Practice

50. Progressive Problem Solving • Avoid the routinization of work • Go past where you have to • Reformulate problems • Add challenging, nuance and difficulty