A Queue-Based Monte Carlo Analysis to Support Decision Making for Implementation of an Emergency Department Fast Track

Abstract

Emergency departments (EDs) are seeking ways to utilize existing resources more efficiently as they face rising numbers of patient visits. This study explored the impact on patient wait times and nursing resource demand from the addition of a fast track, or separate unit for low-acuity patients, in the ED using a queue-based Monte Carlo simulation in MATLAB. The model integrated principles of queueing theory and expanded the discrete event simulation to account for time-based arrival rates. Additionally, the ED occupancy and nursing resource demand were modeled and analyzed using the Emergency Severity Index (ESI) levels of patients, rather than the number of beds in the department. Simulation results indicated that the addition of a separate fast track with an additional nurse reduced overall median wait times by 35.8 ± 2.2 percent and reduced average nursing resource demand in the main ED during hours of operation. This novel modeling approach may be easily disseminated and informs hospital decision-makers of the impact of implementing a fast track or similar system on both patient wait times and acuity-based nursing resource demand.

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

“…The limitation of this model is that it insufficiently addresses patient throughput time in terms of decision-making and cost factors. Fitzgerald, et al [23] proposed an integrated DES and queueing theory model to support decision-making for a fast-tracked ED process. The proposed model informs hospital decision-makers about the effect of fast tracking or similar program implementation on patient waiting times and acuity-based demand for nursing services.…”

“…The issue of patient throughput time is affected by several causative factors, including waiting time, length of stay (LoS), and decision-making [9,22], but existing models have failed to sufficiently handle these causative factors [9] because these models do not sufficiently consider all causative factors that affect the issue of throughput time in ED [18]. Thus, patient throughput time must be improved by minimizing patients' waiting time and LoS and considering decision-making factors [23].…”

Optimizing Patient Waiting Time in the Outpatient Department of a Multi-specialty Indian Hospital: The Role of Technology Adoption and Queue-Based Monte Carlo Simulation

“…Fitzgerald et al [10] have modeled patient flow in Emergency Department of a private, non-profit medical center in central Massachusetts using queue-based Monte Carlo simulation in MATLAB. The model used a combination of queueing theory and discrete-event simulation to analyze time-based arrival rates by generating 300 sets of simulation data for each of the simulation scenario.…”

Optimiser l’accès aux soins de santé dans les services d’urgences pédiatriques : une voie d’accès rapide pour les enfants présentant un problème respiratoire aigu

“…La TVAR est basée sur l'orientation de patients présentant, depuis le triage, une condition clinique relativement stable et ne nécessitant aucune intervention invasive, vers un secteur de l'urgence spécifique pour la prise en charge d'une telle population. Celle-ci permet de débuter immédiatement les traitements et de diminuer les délais de prise en charge (Bussières et al, 2015;Chan et al, 2015;Doetzel et al, 2016;Fitzgerald et al, 2017;Kwa et Blake, 2007;Sanchez et al, 2005;Yarmohammadian et al, 2017). Plusieurs pays, dont les États-Unis (Fitzgerald et al, 2017;Sanchez, Smally, Grant et Jacobs, 2005), la France (Beltramini et al, 2013;Claret et al, 2014) et l'Australie (Gill et al, 2018;Kwa et Blake, 2007) ont déjà expérimenté la réorganisation de leur structure de soins d'urgence avec la TVAR ayant comme résultats une optimisation de l'accès aux soins de santé auprès de clientèles diversifiées.…”

“…Plus spécifiquement, la littérature a démontré que cette méthode diminue le temps d'attente pour les patients, la durée moyenne de séjour (DMS), le nombre de patients qui quittent sans une prise en charge ainsi que les coûts hospitaliers pour chaque patient (Beltramini et al, 2013;Bussières et al, 2015;Chan et al, 2015;Claret et al, 2014;Doetzel et al, 2016;Fitzgerald et al, 2017;Miake-Lye et al, 2017;Parikh, Hall et Teach, 2013;Sanchez et al, 2005). Sans diminuer la qualité des soins offerts, la TVAR permet une meilleure fluidité dans la réponse aux patients dans les urgences adultes et pédiatriques avec un taux élevé de satisfaction des patients et de l'équipe soignante (Beltramini et al, 2013;Chan et al, 2015;Doetzel et al, 2016;Kwa et Blake, 2007;Parikh et al, 2013;Sanchez et al, 2005;Yarmohammadian et al, 2017).…”