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Revista da Associação Médica Brasileira

Print version ISSN 0104-4230On-line version ISSN 1806-9282


AMORIM, Fábio Ferreira et al. Reducing overcrowding in an emergency department: a pilot study. Rev. Assoc. Med. Bras. [online]. 2019, vol.65, n.12, pp.1476-1481.  Epub Jan 24, 2020. ISSN 0104-4230.


Exploring the use of forecasting models and simulation tools to estimate demand and reduce the waiting time of patients in Emergency Departments (EDs).


The analysis was based on data collected in May 2013 in the ED of Recanto das Emas, Federal District, Brasil, which uses a Manchester Triage System. A total of 100 consecutive patients were included: 70 yellow (70%) and 30 green (30%). Flow patterns, observed waiting time, and inter-arrival times of patients were collected. Process maps, demand, and capacity data were used to build a simulation, which was calibrated against the observed flow times. What-if analysis was conducted to reduce waiting times.


Green and yellow patient arrival-time patterns were similar, but inter-arrival times were 5 and 38 minutes, respectively. Wait-time was 14 minutes for yellow patients, and 4 hours for green patients. The physician staff comprised four doctors per shift. A simulation predicted that allocating one more doctor per shift would reduce wait-time to 2.5 hours for green patients, with a small impact in yellow patients’ wait-time. Maintaining four doctors and allocating one doctor exclusively for green patients would reduce the waiting time to 1.5 hours for green patients and increase it in 15 minutes for yellow patients. The best simulation scenario employed five doctors per shift, with two doctors exclusively for green patients.


Waiting times can be reduced by balancing the allocation of doctors to green and yellow patients and matching the availability of doctors to forecasted demand patterns. Simulations of EDs’ can be used to generate and test solutions to decrease overcrowding.

Keywords : Time Management; Emergency Medical Services; Computer Simulation; Health Services Needs and Demand; Patient Satisfaction.

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