Skip to main content
. Author manuscript; available in PMC: 2012 Jul 1.
Published in final edited form as: Int J Med Inform. 2011 Apr 22;80(7):533–540. doi: 10.1016/j.ijmedinf.2011.03.014

Table III.

Performance of electronic medical records (EMR) fields in predicting primary care physician diagnosis of clinical depression

Fields Sensitivity
(%)		 Specificity
(%)		 Area under the
Curve (AUC)
(95% CI)		 Positive
Predictive
Value (%)		 Negative
Predictive
Value (%)
One EMR field
Billing diagnosis of depression 77 76 0.77 (0.73–0.81)1 76 77
Depression on patient problem
list		 49 78 0.63 (0.59–0.68) 68 61
Antidepressants on medication
list		 56 88 0.72 (0.68–0.76) 83 67
Combinations of EMR fields
Billing diagnosis OR
antidepressant on medication list		 85 73 0.79 (0.76–0.83)2 76 84
Billing diagnosis OR problem list 93 60 0.76 (0.73–0.80) 69 90
Billing diagnosis OR problem list
OR antidepressant on medication
list		 93 60 0.76 (0.73–0.80) 69 90
Billing diagnosis AND
antidepressant on medication list		 48 91 0.69 (0.65–0.73)3 83 64
Billing diagnosis AND problem
list		 33 94 0.64 (0.60–0.67)3 85 59
Billing diagnosis AND problem
list AND antidepressant on
medication list		 25 96 0.61 (0.57–0.64)3 86 57
1

The AUC for this field is statistically significant as compared to the AUC for Depression on patient problem list (p<0.001); there is a trend towards significance when compared to the AUC for Antidepressants on medication list (p=0.056).

2

The AUC for this combination is statistically significant as compared to the AUC for Billing diagnosis of depression (p<0.01).

3

The AUC for Billing diagnosis of depression is statistically significant as compared to the AUC of these combinations (p<0.001)