Table 1.
Demographic characteristics of participants at 2 pediatric institutions (N=275).
| Characteristic | SickKids (n=195), n (%) | Lucile Packard Children’s Hospital (n=80), n (%) | P value | ||||
| Male gender | 93 (47.7) | 35 (43.8) | .64 | ||||
| Professional rolea |
|
|
|
||||
|
|
Physician | 165 (84.6) | 73 (91.3) | .20 | |||
|
|
Health system leader | 22 (11.3) | 17 (21.3) | .05 | |||
|
|
Data scientist | 15 (7.7) | 2 (2.5) | .18 | |||
| Physician specialty |
|
|
<.001 | ||||
|
|
Hematology oncology | 33 (16.9) | 14 (17.5) |
|
|||
|
|
General medicine | 21 (10.8) | 7 (8.8) |
|
|||
|
|
Critical care medicine | 11 (5.6) | 12 (15.0) |
|
|||
|
|
Emergency medicine | 14 (7.2) | 0 (0) |
|
|||
|
|
Cardiology | 9 (4.6) | 7 (8.8) |
|
|||
|
|
Neurology | 11 (5.6) | 3 (3.8) |
|
|||
|
|
Endocrinology and metabolism | 10 (5.1) | 6 (7.5) |
|
|||
|
|
Gastroenterology | 9 (4.6) | 0 (0) |
|
|||
|
|
Respirology | 4 (2.1) | 4 (5.0) |
|
|||
|
|
Infectious disease | 2 (1.0) | 5 (6.3) |
|
|||
|
|
Surgery | 0 (0) | 6 (7.5) |
|
|||
|
|
Adolescent medicine | 6 (3.1) | 0 (0) |
|
|||
|
|
Other | 20 (10.3) | 7 (8.8) |
|
|||
|
|
Not known | 45 (23.1) | 9 (11.3) |
|
|||
| Years from completion of training |
|
|
.006 | ||||
|
|
<1 | 6 (3.1) | 0 (0) |
|
|||
|
|
1-4 | 38 (19.5) | 5 (6.3) |
|
|||
|
|
5-10 | 38 (19.5) | 25 (31.3) |
|
|||
|
|
11+ | 113 (57.9) | 50 (62.5) |
|
|||
| Decision-making ability to implement artificial intelligence initiatives | 99 (50.8) | 41 (51.3) | >.99 | ||||
| Number of machine learning models deployed at institution in last 5 years |
|
.43 | |||||
|
|
None | 31 (15.9) | 11 (13.8) |
|
|||
|
|
1 | 7 (3.6) | 6 (7.5) |
|
|||
|
|
2-4 | 14 (7.2) | 9 (11.3) |
|
|||
|
|
5-10 | 2 (1.0) | 1 (1.3) |
|
|||
|
|
11+ | 4 (2.1) | 0 (0) |
|
|||
|
|
Do not know | 137 (70.3) | 53 (66.3) |
|
|||
aRespondent may choose more than 1 option and thus, numbers do not add to 100%.