Background: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a shortage of nursing staff, it is essential to identify the early warning signs of adverse conditions among children as quickly as possible. Current targeted care needs to be improved. Objective: This study aimed to investigate the application of an artificial intelligence (AI)–based version of the Pediatric Early Warning Score (PEWS) in a pediatric emergency observation unit, analyze the relationship between PEWS and disease severity, and assess its impact on the length of hospital stay and hospitalization costs after admission, thereby providing a reference for targeted nursing care. Methods: We performed a retrospective study. A total of 1233 pediatric patients admitted via the pediatric emergency department of a tertiary specialty hospital in Guangzhou from September 2023 to March 2024 were included. The patients were divided according to whether they triggered a PEWS early warning into an early warning group (PEWS ≥1) and a non–early warning group (PEWS=0) during emergency observation. Length of stay and hospitalization costs were compared between the early warning group and the non–early warning group. Differences between groups were assessed using the Mann-Whitney test. We performed multivariable logistic regression to discuss the association of resource use metrics and PEWS status, adjusted by age, sex, and disease category (respiratory, neurological, and hematologic). Results: Of 1233 patients, 597 (48.4%) triggered the PEWS early warning (mean score 2.44, SD 1.41), and 636 (51.6%) did not. In the early warning group, 68 children were transferred to the intensive care unit, with a mean PEWS of 3.32 (SD 1.73). Compared with the non–early warning group, the early warning group had a longer hospital stay (=−5.180; <.001) and higher hospitalization costs (=−6.500; <.001), and the differences between groups were statistically significant (<.001). Among the top 3 admission categories—respiratory, neurological, and hematologic diseases—children in the early warning group had significantly longer hospital stays and higher hospitalization costs (all <.01). The β coefficient for length of hospital stay was 0.053 (SE 0.010; Wald ²=5.533; odds ratio 1.055, 95% CI 1.035‐1.075), while the β coefficient for hospitalization costs was 0.001 (SE 0.000; Wald ²=6.075; odds ratio 1.001, 95% CI 1.001‐1.001). Conclusions: Compared with the non–early warning group, the early warning group had significantly longer hospital stays and higher hospitalization costs (<.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all <.01). These findings show differences between children who triggered the warning and children who did not, providing a reference for identifying critically ill children for targeted care.
