JMIR Publications

ABSTRACT

Background:

There are a large number of pediatric emergency patients. Due to the fact that the children cannot describe their own conditions, there is a shortage of nursing staff. It is extremely important to be able to identify the early warning signs of the children's conditions as early as possible. The current targeted care needs to be improved.

Objective:

This study aimed to investigate the application of an artificial intelligence–based pediatric early warning score (PEWS) in the pediatric emergency observation unit, analyze the relationship between PEWS and disease severity as well as patient disposition, and assess its impact on length of hospital stay and hospitalization costs after admission, so as to provide references for targeted nursing care.

Methods:

A total of 1,233 pediatric patients admitted via the pediatric emergency department of a tertiary specialty hospital in Guangzhou from September 2023 to March 2024 were included. Length of stay and hospitalization costs were compared between the early warning group and the non–early warning group.

Results:

The mean PEWS in the early warning group was 2.44 ± 1.41. In the early warning group, 68 children were transferred to the intensive care unit, with a mean PEWS of 3.32 ± 1.73. Compared with the non–early warning group, the early warning group had a longer hospital stay (z = −5.180, P < 0.001) and higher hospitalization costs (z = −6.500, P < 0.001), and the differences between groups were statistically significant (P < 0.001). Compared with the non–early warning group, among the top three admission categories—respiratory, neurological, and hematologic diseases—children in the PEWS early warning group had significantly longer hospital stays and higher hospitalization costs, with statistically significant differences between groups (P < 0.01).

Conclusions:

An artificial intelligence–based pediatric early warning score helps to identify critically ill children in a timely and accurate manner, rapidly distinguish those who require prioritized nursing care from those who can safely wait, and enable more targeted medical and nursing interventions.