Serious Games for Training in Patient Flow Management in Emergency Departments:  State of the Art and Perspectives

Blot, Charlotte; Weickert, Katia; Payros, Arthur; Avalos, Marta; Gil-Jardiné, Cédric

doi:10.32473/flairs.36.133276

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…16,18,[20][21][22] It also offers some real world suggestions, based on actual ED data, for predicting patient flow within the caveats of the resources available at this site, as suggested by recent reviews. 23…”

Section: Discussionmentioning

confidence: 99%

Modelling a two‐stream emergency department segregation and admission system from COVID‐19 early rapid antigen testing: A pilot study

Johnston,

Wong,

Appo

et al. 2023

Emerg Medicine Australasia

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ObjectiveMany factors influence patient flow through an ED, including streaming, treatment spaces and staff resources. This pilot study explored and compared real time patient flow using a single‐stream system versus varying configurations of possible two‐stream systems using computer simulation.MethodsSimulation modelling was used to assess the delay in treatment of a rapid‐antigen‐tested‐based, two‐stream model for patient flow through ED during the peak phase of the COVID pandemic.ResultsModelling two‐stream configuration for all patients (minimum time to be seen for both COVID‐positive and COVID‐negative patients) showed that in the case study ED, a two‐stream system and linked changes in bed configuration for managing the risks of infection can impact delays in treatment.ConclusionsData‐driven modelling within specific clinical settings can inform the (in)efficiency of patient flow processes and help clinicians and managers make evidence‐based decisions about patient transition through EDs. This can assist with reconfiguration of ED patient streaming particularly during periods of unique need, such as the recent COVID‐19 pandemic.

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