Table 4.

Model Agreement and Specialty Match Using 2016 Data

Specialty	Count	Models Predicting the Same Specialty, %	Specialty Match, %a	Specialty Mismatch, %b
Allergy/immunology	1,625	97.1	89.6	7.5
Anesthesiology	16,110	97.9	94.3	3.6
Cardiology	11,170	96.9	90.4	6.5
Dermatology	5,498	98.8	96.7	2.1
Emergency medicine	18,663	98.3	87.0	11.3
Endocrinology	2,497	95.8	83.3	12.5
Gastroenterology	5,960	97.2	92.4	4.8
Hematology-oncology	5,572	94.9	84.9	10.0
Infectious disease	2,328	91.1	61.2	29.9
Nephrology	3,691	96.7	86.9	9.8
Neurology	6,217	94.5	83.1	11.4
Neurosurgery	2,008	80.6	48.3	32.3
Obstetrics and gynecology	11,505	96.7	90.6	6.1
Ophthalmology	8,755	99.1	97.9	1.2
Orthopedic surgery	11,095	94.6	86.1	8.5
Otolaryngology	4,262	96.8	89.5	7.3
Pathology	4,831	99.3	97.8	1.5
Physical medicine and rehabilitation	3,438	83.2	41.6	41.6
Plastic surgery	1,795	80.7	42.2	38.5
Primary care	101,498	98.3	92.6	5.7
Psychiatry	14,974	97.9	92.1	5.8
Pulmonology	5,395	96.1	83.2	12.9
Radiation Oncology	1,903	95.9	91.0	4.9
Radiology	11,816	99.1	96.4	2.7
Rheumatology	2,030	97.6	91.7	5.9
Surgery	13,278	91.7	77.7	14.0
Urology	4,579	97.3	94.5	2.8
Overall	282,493	97.0c	89.4c	7.6c

For this analysis, we applied the 2014, 2015, and 2016 combined random forests to 2016 Test data, for a total of 3 predictions based on prescribing and procedure data for a single year. Model agreement is defined as all 3 models predicting the same specialty.

^aAll 3 models predicted the self-reported specialty.

^bAll 3 models predicted a specialty that differed from the self-reported category.

^cMean across all specialties weighted by number in each specialty.