Reducing overcrowding in an emergency department: a pilot study

• Published in: Revista da Associação Médica Brasileira (1992) 2019-12, Vol.65 (12), p.1476-1481
• Main Authors: Amorim, Fábio Ferreira, Almeida, Karlo Jozefo Quadros de, Barbalho, Sanderson Cesar Macedo, Balieiro, Vanessa de Amorim Teixeira, Machado Neto, Arnaldo, Dias, Guilherme de Freitas, Santana, Levy Aniceto, Aguiar, Cristhiane Pinheiro Teixeira Gico de, Silva, Cláudia Cardoso Gomes da, Dasu, Sriram
• Format: Magazinearticle
• Language: English
• ISSN: 0104-4230; 1806-9282
• DOI: 10.1590/1806-9282.65.12.1476

SUMMARY OBJECTIVE Exploring the use of forecasting models and simulation tools to estimate demand and reduce the waiting time of patients in Emergency Departments (EDs). METHODS 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. RESULTS 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. CONCLUSION 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. RESUMO OBJETIVO Explorar o uso de modelos de previsão e ferramentas de simulação para estimar a demanda e reduzir o tempo de espera dos pacientes em Departamentos de Emergência (DE). METODOLOGIA A análise foi baseada em dados coletados em maio de 2013, no DE do Recanto das Emas, Distrito Federal, Brasil, que utiliza o Protocolo de Manchester como sistema de triagem. Um total de 100 pacientes consecutivos foram incluídos: 70 amarelos (70%) e 30 verdes (30%). Padrões de fluxo, tempo de espera observado e tempos entre as chegadas dos pacientes foram registrados. Mapas de processo, demanda e dados de capacidade foram utilizados na construção de uma simulação que foi calibrada de acordo com o fluxo observado. Uma análise do tipo “e se...” foi conduzida para reduzir os tempos de espera. RESULTADOS O