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Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study
Wanhua Xie1, Xuan Shi2, Meiqing Peng1
1Outpatient Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, 9 Jinsui Road, Guangzhou, Guangdong, 510623, China, 86 13725370379, 86 2038076020.
Insights
The AI-based Pediatric Early Warning Score (PEWS) effectively identifies children needing intensive care, leading to longer hospital stays and higher costs. This tool aids in recognizing critically ill pediatric patients for targeted interventions.
Area of Science:
- Pediatric Emergency Medicine
- Artificial Intelligence in Healthcare
- Clinical Decision Support Systems
Background:
- Pediatric emergency departments face high patient volumes and nursing shortages.
- Accurate and rapid identification of early warning signs in children is crucial for timely intervention.
- Current targeted care protocols require enhancement for improved patient outcomes.
Purpose of the Study:
- To evaluate an AI-based Pediatric Early Warning Score (PEWS) in a pediatric emergency observation unit.
- To analyze the correlation between PEWS scores and pediatric disease severity.
- To assess the impact of PEWS on hospitalization length and costs for targeted nursing care.
Main Methods:
- Retrospective study of 1233 pediatric patients admitted via emergency departments.
- Patients categorized into 'early warning' (PEWS ≥1) and 'non-early warning' (PEWS=0) groups.
- Comparison of length of stay and hospitalization costs using Mann-Whitney U test and multivariable logistic regression.
Main Results:
- Nearly half of patients (48.4%) triggered a PEWS early warning.
- The early warning group experienced significantly longer hospital stays and higher costs (P<.001).
- This trend persisted across respiratory, neurological, and hematologic disease categories (P<.01).
Conclusions:
- AI-based PEWS identifies children with increased resource utilization and severity.
- Early warning scores correlate with longer hospital stays and elevated costs.
- Findings support PEWS as a valuable tool for identifying critically ill children for targeted care.
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 U 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 (z=-5.180; P<.001) and higher hospitalization costs (z=-6.500; P<.001), and the differences between groups were statistically significant (P<.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 P<.01). The β coefficient for length of hospital stay was 0.053 (SE 0.010; Wald χ²1=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 χ²1=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 (P<.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all P<.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.