Forecasting the Length-of-Stay of Pediatric Patients in Hospitals: A Scoping Review

Abstract

Background: Healthcare management faces complex challenges in allocating hospital resources and predicting patients' length-of-stay (LOS) is critical in effectively managing those resources. This work aims to identify approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS–P), and patients' populations and environments used to develop the models. Methods: Based on a scoping review, 28 studies were identified and analyzed. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation. Results: Forecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and mostly used in emergency departments to model patients in specific situations.Conclusions: The studies' main benefits include informing family members about the patient's expected discharge date and enabling hospital resources' allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to methods applied for LOS–P forecasting and a future research agenda.