{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:47:54Z","timestamp":1782841674687,"version":"3.54.5"},"reference-count":27,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to investigate the application of an artificial intelligence (AI)\u2013based 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n 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 \u22651) and a non\u2013early warning group (PEWS=0) during emergency observation. Length of stay and hospitalization costs were compared between the early warning group and the non\u2013early warning group. Differences between groups were assessed using the Mann-Whitney\n U<\/jats:italic>\n 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).\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n 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\u2013early warning group, the early warning group had a longer hospital stay (\n z<\/jats:italic>\n =\u22125.180;\n P<\/jats:italic>\n <.001) and higher hospitalization costs (\n z<\/jats:italic>\n =\u22126.500;\n P<\/jats:italic>\n <.001), and the differences between groups were statistically significant (\n P<\/jats:italic>\n <.001). Among the top 3 admission categories\u2014respiratory, neurological, and hematologic diseases\u2014children in the early warning group had significantly longer hospital stays and higher hospitalization costs (all\n P<\/jats:italic>\n <.01). The \u03b2 coefficient for length of hospital stay was 0.053 (SE 0.010; Wald\n \u03c7<\/jats:italic>\n \u00b2\n 1<\/jats:sub>\n =5.533; odds ratio 1.055, 95% CI 1.035\u20101.075), while the \u03b2 coefficient for hospitalization costs was 0.001 (SE 0.000; Wald\n \u03c7<\/jats:italic>\n \u00b2\n 1<\/jats:sub>\n =6.075; odds ratio 1.001, 95% CI 1.001\u20101.001).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n Compared with the non\u2013early warning group, the early warning group had significantly longer hospital stays and higher hospitalization costs (\n P<\/jats:italic>\n <.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all\n P<\/jats:italic>\n <.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.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/89306","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T12:35:07Z","timestamp":1776429307000},"page":"e89306-e89306","source":"Crossref","is-referenced-by-count":0,"title":["Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional 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