Efficient management of neonatal sepsis diagnosis using predictive analytics methods: a scoping review - KVKL Narasimha Rao; Pradeep Kumar Dadabada; Sanjita Jaipuria (Healthcare in Low-resource Settings)
Neonatal sepsis is a critical and life-threatening condition that significantly contributes to the high rates of illness and death among newborns, particularly in low- and middle-income countries (LMICs). The complexity of diagnosing and treating neonatal sepsis arises from the unique physiological characteristics of newborns and the increasing challenge of antibiotic resistance. However, early diagnosis and prompt treatment are crucial in effectively managing this condition. Predictive analytics methods can potentially address the gaps in diagnosing and treating neonatal sepsis, especially in resource-constrained settings. This scoping review aims to comprehensively analyze the critical factors involved in neonatal sepsis diagnosis and the potential impact of utilizing predictive analytics models in its diagnosis and treatment. This paper reviews the literature to determine the critical factors in managing neonatal sepsis efficiently. It will also delve into the ability of predictive analytics methods to diagnose neonatal sepsis at an early stage, reduce the usage of antibiotics, and achieve cost savings in treatment, highlighting the overall efficiency of diagnosing and managing neonatal sepsis. The findings of this review could provide insight into the impact of predictive analytics methods for diagnosing and treating neonatal sepsis in hospitals in low-resource settings. The review reveals that the predictive analytics methods could lead to efficient management of neonatal sepsis through analysis of critical factors such as early diagnosis of neonatal sepsis at least 48 hours before clinical manifestation, reduction in antibiotic treatment by at least 33-97%, and reduction in cost of therapy by 4.1-50.4%.