Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aplin, S., Baines, D., DE Lima, J.: Use of the ASA physical status grading system in pediatric practice. Paediatr. Anaesth. 17(3), 216–222 (2007)
Beyer, J.E., Turner, S.B., Jones, L., Young, L., Onikul, R., Bohaty, B.: The alternate forms reliability of the Oucher pain scale. Pain Manag. Nurs. 6(1), 10–17 (2005)
Bonadio, W.A., Hennes, H., Smith, D., Ruffing, R., Melzer-Lange, M., Lye, P., Isaacman, D.: Reliability of observation variables in distinguishing infectious outcome of febrile young infants. Pediatr. Infect. Dis. J. 12(2), 111–114 (1993)
Carrol, E., Riordan, F., Thomson, A., Sills, J., Hart, C.: The role of the Glasgow meningococcal septicaemia prognostic score in the emergency management of meningococcal disease. Arch. Dis. Child. 81(3), 278 (1999). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1718049/
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). http://dx.doi.org/10.1007/BF00994018
Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: data mining toolbox in python. J. Mach. Learn. Res. 14, 2349–2353 (2013). http://jmlr.org/papers/v14/demsar13a.html
Ferres, J.: Comparison of two nebulized treatments in wheezing infants. Eur. Respir. J. 1, 306 (1988)
FitzGerald, G.: Triage revisited. Emerg. Med. 10(4), 291–293 (1998). http://dx.doi.org/10.1111/j.1442-2026.1998.tb00694.x
Herndon, R.: Handbook of Neurologic Rating Scales, 2nd edn. Demos Medical Publishing, New York (2006). http://books.google.com.co/books?id=w1yPmehSZ2cC
Holden, R.J.: Lean thinking in emergency departments: a critical review. Ann. Emerg. Med. 57(3), 265–278 (2010). http://dx.doi.org/10.1016/j.annemergmed.2010.08.001
Huppler, A.R., Eickhoff, J.C., Wald, E.R.: Performance of low-risk criteria in the evaluation of young infants with fever: review of the literature. Pediatrics 125(2), 228–233 (2010). http://pediatrics.aappublications.org/content/125/2/228
Ieraci, S., Digiusto, E., Sonntag, P., Dann, L., Fox, D.: Streaming by case complexity: evaluation of a model for emergency department fast track. Emerg. Med. Australas. 20(3), 241–249 (2008). http://dx.doi.org/10.1111/j.1742-6723.2008.01087.x
Jolliffe, I.: Principal Component Analysis. Springer Series in Statistics. Springer, Berlin (2002). http://books.google.com.co/books?id=TtVF-ao4fI8C
Kelly, A.M., Bryant, M., Cox, L., Jolley, D.: Improving emergency department efficiency by patient streaming to outcomes-based teams. Aust. Health Rev. 31(1), 16–21 (2007). http://www.publish.csiro.au/paper/AH070016
McCarthy, P.L., Sharpe, M.R., Spiesel, S.Z., Dolan, T.F., Forsyth, B.W., DeWitt, T.G., Fink, H.D., Baron, M.A., Cicchetti, D.V.: Observation scales to identify serious illness in febrile children. Pediatrics 70(5), 802–809 (1982). http://pediatrics.aappublications.org/content/70/5/802
McCullagh, P., Nelder, J.: Generalized Linear Models, 2nd edn. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Taylor & Francis, Abingdon-on-Thames (1989). http://books.google.co.uk/books?id=h9kFH2_FfBkC
Mintegui, R.S., Sanchez, E.J., Benito, F.J., Angulo, B.P., Gastiasoro, C.L., Ortiz, A.A.: Usefulness of oxygen saturation in the assessment of children with moderated laryngitis. An. Esp. Pediatr. 45(3), 261–263 (1996)
Ng, D., Vail, G., Thomas, S., Schmidt, N.: Applying the lean principles of the Toyota production system to reduce wait times in the emergency department. CJEM 12(1), 50–57 (2010)
Paganini, H., de Santolaya, P., Álvarez, M., Ara\({\tilde{n}}\)a Rosaínz, M.D.J., Arteaga Bonilla, R., Bonilla, A., Caniza, M., Carlesse, F., López, P., Due\({\tilde{n}}\)aas de Chicas, L., de León, T., del Pont, J.M., Melgar, M., Naranjo, L., Odio, C., Rodríguez, M., Scopinaro, M.: Diagnóstico y tratamiento de la neutropenia febril en ni\({\tilde{n}}\)os con cáncer. consenso de la sociedad latinoamericana de infectología pediátrica. Revista chilena de infectología 28, 10–38 (2011). http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-10182011000400003&nrm=iso
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Parallel distributed processing: explorations in the microstructure of cognition. In: Learning Internal Representations by Error Propagation, vol. 1., pp. 318–362. MIT Press, Cambridge (1986). http://dl.acm.org/citation.cfm?id=104279.104293
Scarfone, R.J., Fuchs, S.M., Nager, A.L., Shane, S.A.: Controlled trial of oral prednisone in the emergency department treatment of children with acute asthma. Pediatrics 92(4), 513–518 (1993)
Velasco-Pérez, G.: Escalera analgésica en pediatría. Acta pediátrica de México 35, 249–255 (2014). http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0186-23912014000300011&nrm=iso
Womack, J.P., Jones, D.T., Roos, D.: The Machine That Changed the World: The Story of Lean Production. The MIT International Motor Vehicle Program. HarperCollins, New York (1991). https://books.google.de/books?id=Jz4zog27W7gC
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Caicedo-Torres, W., García, G., Pinzón, H. (2016). A Machine Learning Model for Triage in Lean Pediatric Emergency Departments. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-47955-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47954-5
Online ISBN: 978-3-319-47955-2
eBook Packages: Computer ScienceComputer Science (R0)