1. Diagnostic decision-making: How clinicians think
Jerome R Hoffman, MA, MD
Professor of Medicine Emeritus, UCLA School of Medicine

2. MEASLES: miserable, rash on face

3. Measles = MISERABLE

4. MEASLES: diffuse rash (on butt)

5. Koplik’s spots

7. Exudate (as in GAS)

22. Perception We usually see what we expect to see
We try to impose coherence and order
What we see depends on
context
framing
vantage point
complexity (“background noise”)

24. Cognitive Error: A Few Key Heuristics
Anchoring
The importance of first impressions
Ascertainment Bias
Looking for what you expect to see
Confirmation bias
Ignoring the inconvenient, overvaluing what seems to fit
Diagnostic Momentum / Premature Closure

25. Cognitive Error: A Few Key Heuristics
Inattention Blindness (“Attentional Deficit”)
The gorilla in the room
Political momentum
When you need a success …
Say it enough times, and … it’s true
Citation authority
The greatest impediment to a new diagnosis …
Schizophrenics are immortal!

26. “Search Satisfying” (the world oughta make sense)
“I thought about her, and she called!”
“I got this lump reaching for something.”
“Abdominal pain following fish tacos”

27. Cognitive Errors: Heuristics
Ordering Effect
We remember the opening
(and occasionally the close)
Framing
95% chance of survival vs 5% chance of dying
Triage - what room he’s in
Change of shift
Pitching the diagnosis to a consultant

28. Cognitive Error: Heuristics
Availability (why we don’t know about ASA)
The last case I saw
The most dramatic case I ever saw
Gambler’s Fallacy
If I just had six heads in a row …
You can’t see 3 SAH’s in 1 day

29. Cognitive Errors: Heuristics
Sutton’s Slip (Playing the Odds)
“… because that’s where the money is.”
The opposite of “worst first”
Looking for Zebras (“Base-rate Neglect”)
Uncommon presentations of common disease are more common than typical presentations of rare ones

30. Let’s Not Forget About Bias
Gender, ethnicity
Psychiatric disease, intoxication, homelessness,
“Drug seekers”
“Frequent fliers”
Patients with diseases we don’t understand

32. Hypothesis Testing: Bayesian Approach
Estimate of Prior Probability
How do we know?
Would we agree?
Thresholds
How should we choose?
1 or 2?

33. Hypothesis Testing: Bayesian Approach
A good test (LP or CBC?)
Accurate
Reliable
A test whose characteristics we know
On what evidence?
What gold standard?
A test applied to the right question
BNP
D-dimer

34. 2 x 2 tables Sensitivity / specificity Predictive values definitions
“SpIn” and “SnOut”
Predictive values
can look much better!

35. 2 x 2 tables Sensitivity / specificity Predictive values
down single columns only
“unchanged” by prevalence of disease
Predictive values
across columns
changed greatly by prevalence

36. 2x2 for s/sp = 90/60% Disease + - Test sn = 90% sp = 60% 20 80
prevalence = 20%

37. 2x2 for s/sp = 90/60% Disease + - Test 18 32 sn = 90% 2 48 sp = 60% 2080
prevalence = 20%

38. 2x2 for s/sp = 90/60% Disease + - Test 18 32 sn = 90% 2 48 sp = 60% 2080
prevalence = 20%
ppv = 18/50 = 36%
npv = 48/50 = 96%

39. 2x2 for s/sp = 90/60% Disease + - Test sn = 90% sp = 60% 80 20
prevalence = 80%

40. 2x2 for s/sp = 90/60% Disease + - Test 72 8 sn = 90% 12 sp = 60% 80 20
prevalence = 80%

41. 2x2 for s/sp = 90/60% Disease + - Test 72 8 sn = 90% 12 sp = 60% 80 20
prevalence = 80%
ppv = 72/80 = 90%
npv = 12/20 = 60%

42. 2x2 for s/sp = 90/60% Disease + - Test 18 32 sn = 90% 2 48 sp = 60% 2080
prevalence = 20%
ppv = 18/50 = 36%
npv = 48/50 = 96%

43. 2x2 for s/sp = 90/60% Disease + - Test 72 8 sn = 90% 12 sp = 60% 80 20
prevalence = 80%
ppv = 72/80 = 90%
npv = 12/20 = 60%

44. Graph # 1: Bayesian Graph With Treatment Threshold

45. Graph # 2:. Bayesian Graph with Treatment Threshold
Graph # 2: Bayesian Graph with Treatment Threshold (with poor test characteristics)

46. Graph # 3: Bayesian Graph with Treatment Threshold (with great test characteristics)

47. Graph # 4: Bayesian Graph with Treatment Threshold

48. Graph # 5: Bayesian Graph with Treatment Threshold Rheumatic Fever Epidemic

49. NPV instead of s/sp Disease + - Test sn = 50% sp = 90% 80 20
prevalence = 80%

50. NPV instead of s/sp Disease + - Test 40 2 sn = 50% 18 sp = 90% 80 20
prevalence = 80%

51. NPV instead of s/sp Disease + - Test 40 2 sn = 50% 18 sp = 90% 80 20
prevalence = 80%
ppv = 40/42 = 95%
npv = 18/58 = 31%

52. NPV instead of s/sp Disease + - Test 40 20 sn = 50% 180 sp = 90% 80
200
prevalence = 29%
ppv = 40/60 = 67%
npv = 180/220 = 82%

53. NPV instead of s/sp Disease + - Test 40 200 sn = 50% 1800 sp = 90% 80
2000
prevalence = 4%
ppv = 40/240 = 17%
npv = 1800/1840 = 98%

54. NPV instead of s/sp Disease + - Test 40 2000 sn = 50% 18000 sp = 90%
20000
prevalence = 0.4%
ppv = 40/2040 = 2%
npv = 18000/18040 = 99.8%

55. A Fabulous Test … + the right question
Disease + -
Test
sn = 99%
sp = 98%
1000
prevalence = 50%

56. A Fabulous Test … + the right question
Disease + -
Test
990 20
sn = 99% 10
980
sp = 98%
1000
prevalence = 50%

57. A Fabulous Test … + the right question
Disease + -
Test
990 20
sn = 99% 10
980
sp = 98%
1000
prevalence = 50%
ppv = 990/1010 = 98%
npv = 980/990 = 99%

58. A Fabulous Test … (misapplied)
Disease + -
Test
sn = 99%
sp = 98%
1000
999000
prevalence = 0.1%

59. A Fabulous Test … (misapplied)
Disease + -
Test
990
1980
sn = 99% 10
97020
sp = 98%
1000
100000
prevalence = 0.1%

60. A Fabulous Test … (misapplied)
Disease + -
Test
990
1980
sn = 99% 10
97020
sp = 98%
1000
99000
prevalence = 0.1%
ppv = 990/2970 = 33%
npv = … 99.99%