Monitoring Patient Flow in the One Emergency Care Unit (UPA) in São Paulo Brazil

Pereira, Newton Narciso; Fonseca, Patrick Fernandes Ribeiro da; Viera, Luis Enrique Valdiviezo; Queiroz, Thaís Lessa; Calado, Robisom Damasceno

doi:10.1016/j.ifacol.2022.09.453

Cited by 1 publication

(2 citation statements)

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“…Some data, depending on the case of the study, also exhibit variances when compared to the data utilized in this study. However, these discrepancies do not invalidate the proposals presented in this research, which can showcase alternatives to address the issue of prioritizing patients in emergency department overcrowding and discuss the results of these alternatives (Amorim et al, 2019;Pereira et al, 2022;Bouzon Nagem Assad & Spiegel, 2020).…”

Section: Discussionmentioning

confidence: 72%

“…The Manchester Model is one of the most used in Brazil and presents five categories of signs and symptoms based on a patient's complaint or presenting situation (Amorim et al, 2019;Pereira et al, 2022;Bouzon Nagem Assad & Spiegel, 2020). Each category is associated with a number, a color, a name, and a maximum time for the first contact with the physician.…”

Section: Introductionmentioning

confidence: 99%

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Discrete Event Simulation to Analyze Patient Priorization in the Context of an Emergency Department

Blanco,

Pinheiro,

dos Santos

2024

Pesqui. Oper.

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With the growth of the demand flow and its variability at an emergency department, the complexity to deal with the prioritization of assistance also increases, seeking to offer a quality service. In this sense, several systematized risk triage protocols were created to organize the queues at the emergency, establishing an order of service to ensure that patients do not wait more than a safe time to receive their first medical care. Therefore, the objective of this work is to analyze and evaluate the behavior of an emergency department using a discrete event simulation model. For the results, it was possible to better understand the patient flow, identify problems and difficulties in the system, propose alternatives to deal with patient prioritization, and evaluate the performance of these alternatives through the Goal Question Metric methodology.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%