A machine learning model for triage in lean pediatric emergency departments

High demand periods and under-staffing due to financial constraints cause Emergency Departments (EDs) to frequently exhibit over-crowding and slow response times to provide adequate patient care. In response, Lean Thinking has been applied to help alleviate some of these issues and improve patient handling, with success. Lean approaches in EDs include separate patient streams, with low-complexity patients treated in a so-called Fast Track, in order to reduce total waiting time and to free-up capacity to treat more complicated patients in a timely manner. In this work we propose the use of Machine Learning techniques in a Lean Pediatric ED to correctly predict which patients should be admitted to the Fast Track, given their signs and symptoms. Charts from 1205 patients of the emergency department of Hospital Napoleón Franco Pareja in Cartagena - Colombia, were used to construct a dataset and build several predictive models. Validation and test results are promising and support the validity of this approach and further research on the subject. © Springer International Publishing AG 2016.

• Fecha de publicación: 2016
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/8997
• Acceso en línea: https://hdl.handle.net/20.500.12585/8997
• Palabra clave: Emergency department; Fast track; Lean; Logistic regression; Machine learning; Neural networks; PCA; SVM; Triage; Artificial intelligence; Complex networks; Emergency rooms; Hospitals; Neural networks; Patient monitoring; Patient treatment; Pediatrics; Emergency departments; Fast tracks; Lean; Logistic regressions; Triage; Learning systems
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/