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Forecasting the length-of-stay of pediatric patients in hospitals: a scoping review
Natália B Medeiros1, Flavio S Fogliatto2, Miriam K Rocha3
1Department of Industrial Engineering, Universidade Federal do Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° andar, Porto Alegre, 90035-190, Brazil.
Insights
Predicting pediatric patients' length-of-stay (LOS-P) is crucial for hospital resource management. This review maps forecasting models and their application in pediatric care, identifying key benefits and research gaps.
Area of Science:
- Healthcare Management
- Pediatric Medicine
- Data Science
Background:
- Effective hospital resource allocation hinges on accurate patient length-of-stay (LOS) predictions.
- Forecasting LOS for pediatric patients (LOS-P) is critical for operational efficiency.
- This study maps current approaches to LOS-P forecasting models and their application contexts.
Purpose of the Study:
- To systematically map and analyze existing methods for forecasting pediatric patients' length-of-stay (LOS-P).
- To identify patient populations and hospital environments where LOS-P models are developed and applied.
- To provide a guide for LOS-P forecasting methods and outline future research directions.
Main Methods:
- A scoping review was conducted using the PRISMA-ScR methodology.
- Searches across four major databases (Science Direct, Scopus, Web of Science, Medline) identified 28 relevant studies.
- Methods were classified by stage: data pre-processing, variable selection, and cross-validation.
Main Results:
- Forecasting models are predominantly applied to newborns in neonatal intensive care units.
- Regression analysis is the most common modeling technique.
- Machine learning approaches are emerging, mainly in emergency departments for specific patient scenarios.
Conclusions:
- LOS-P forecasting aids in informing families and optimizing hospital resource allocation.
- Significant research gaps exist in model generalization and practical hospital management applicability.
- The study offers a practical guide and a future research agenda for LOS-P forecasting.
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 map approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS-P) and patients' populations and environments used to develop the models.
Methods:
Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology, we performed a scoping review that identified 28 studies and analyzed them. The search was conducted on four databases (Science Direct, Scopus, Web of Science, and Medline). The identification of relevant studies was structured around three axes related to the research questions: (i) forecast models, (ii) hospital length-of-stay, and (iii) pediatric patients. Two authors carried out all stages to ensure the reliability of the review process. Articles that passed the initial screening had their data charted on a spreadsheet. 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 primarily 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 LOS-P forecasting methods and a future research agenda.
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