Automated Alerts Systems for Pediatric Sepsis Patients: A Systematic Review - Mahmasoni Masdar; Ariani Arista Putri Pertiwi; Tiara Royani; Nana Caterina Sandi; Muhammad Ulin Nuha; Fransiska Regina Cealy; Alessandra Hernanda Soselisa; Suprihatiningsih Suprihatiningsih; Desi Dwi Siwi Atika Dewi (Journal of Nursology)
Objective: Pediatric sepsis is difficult to identify due to subtle symptoms, and early aggressive management is crucial to prevent septic shock. Artificial intelligence can improve sepsis detection by triggering alerts based on patient data. No systematic review has yet discussed AI use for pediatric sepsis screening. This study aims to answer: “What tools alert healthcare providers to the onset of sepsis in pediatric patients in hospitals?”Methods: The study protocol was registered with PROSPERO (CRD42023467930). We searched PubMed, ProQuest, ScienceDirect, Scopus, and EBSCO, focusing on pediatric hospital settings using tools for early sepsis detection, excluding studies on non-sepsis patients, and limiting inclusion to English literature reviews without a publication year restriction. The Joanna Briggs Institute (JBI) Appraisal Tool evaluated study quality, and findings were synthesized qualitatively.Results: Out of 16 articles, four tools for automatic sepsis alerts in pediatrics were identified: Electronic Medical Records (EMR), Electronic Health Records (EHR), The Electronic Alert System (EAS), and The Newborn Cry Diagnostic System (NCDS). EHR is the most commonly used. These tools require various data, such as vital signs, lab results, skin condition, capillary refill, and even a baby's cry.Conclusion: Automated sepsis alerts in pediatrics enhance diagnostic accuracy, expedite decision-making, and decrease sepsis-related mortality. Limitations include language restrictions and the inability to assess each tool's effectiveness or identify the optimal sepsis detection algorithm, underscoring the need for further research, including a meta-analysis.