Applying Multi-phase DES Approach for Modelling the Patient Journey Through Accident and Emergency Departments
Accident and Emergency departments (A&ED) are in charge of providing access to patients requiring urgent acute care. A&ED are difficult to model due to the presence of interactions, different pathways and the multiple outcomes that patients may undertake depending on their health status. In addition, public concern has focused on the presence of overcrowding, long waiting times, patient dissatisfaction and cost overruns associated with A&ED. There is then a need for tackling these problems through developing integrated and explicit models supporting healthcare planning. However, the studies directly concentrating on modelling the A&EDs are largely limited. Therefore, this paper presents the use of a multi-phase DES framework for modelling the A&ED and facilitating the assessment of potential improvement strategies. Initially, the main components, critical variables and different states of the A&ED are identified to correctly model the entire patient journey. In this step, it is also necessary to characterize the demand in order to categorize the patients into pipelines. After this, a discrete-event simulation (DES) model is developed. Then, validation is conducted through the 2-sample t test to demonstrate whether the model is statistically comparable with the real-world A&ED department. This is followed by the use of Markov phase-type models for calculating the total costs of the whole system. Finally, various scenarios are explored to assess their potential impact on multiple outcomes of interest. A case study of a mixed-patient environment in a private A&E department is provided to validate the effectiveness of the multi-phase DES approach.
KeywordsDiscrete-event simulation (DES) Healthcare modelling Accident and emergency department (A&ED) Phase-type models
- 1.West, R.: Objective standards for the emergency services: emergency admission to hospital. J. R. Soc. Med. 94(Suppl. 39), 4 (2001)Google Scholar
- 2.Dolan, B., Holt, L. (eds.): Accident & Emergency: Theory into Practice. Elsevier Health Sciences (2013)Google Scholar
- 21.Ortiz, M.A., López-Meza, P.: Using computer simulation to improve patient flow at an outpatient internal medicine department. In: García, C.R., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds.) UCAmI 2016. LNCS, vol. 10069, pp. 294–299. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48746-5_30CrossRefGoogle Scholar
- 22.Ortiz, M.A., McClean, S., Nugent, C.D., Castillo, A.: Reducing appointment lead-time in an outpatient department of gynecology and obstetrics through discrete-event simulation: a case study. In: García, C.R., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds.) UCAmI 2016. LNCS, vol. 10069, pp. 274–285. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48746-5_28CrossRefGoogle Scholar
- 23.Nuñez-Perez, N., Ortíz-Barrios, M., McClean, S., Salas-Navarro, K., Jimenez-Delgado, G., Castillo-Zea, A.: Discrete-event simulation to reduce waiting time in accident and emergency departments: a case study in a district general clinic. In: Ochoa, Sergio F., Singh, P., Bravo, J. (eds.) UCAmI 2017. LNCS, vol. 10586, pp. 352–363. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67585-5_37CrossRefGoogle Scholar