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
. Escola Superior de Ciências da Saúde, Brasília, DF, Brasil.
. Universidade de Brasília, Brasília (DF). Centro de Apoio e Desenvolvimento Tecnológico - Campus Universitário Darcy Ribeiro, Brasília, DF, Brasil.
Rev Assoc Med Bras (1992). 2019 Dec;65(12):1476-1481. doi: 10.1590/1806-9282.65.12.1476.
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.
探索使用预测模型和模拟工具来估计急诊科患者需求并减少其等待时间。
该分析基于2013年5月在巴西联邦区雷坎托达斯埃马斯急诊科收集的数据，该科室采用曼彻斯特分诊系统。共纳入100例连续患者：70例黄色（70%）和30例绿色（30%）。收集了患者的流程模式、观察到的等待时间和到达间隔时间。使用流程图、需求和容量数据构建模拟，并根据观察到的流程时间进行校准。进行了假设分析以减少等待时间。
绿色和黄色患者的到达时间模式相似，但到达间隔时间分别为5分钟和38分钟。黄色患者的等待时间为14分钟，绿色患者为4小时。医师团队每班有四名医生。模拟预测，每班多分配一名医生将使绿色患者的等待时间减少至2.5小时，对黄色患者的等待时间影响较小。维持四名医生并专门为绿色患者分配一名医生将使绿色患者的等待时间减少至1.5小时，黄色患者的等待时间增加15分钟。最佳模拟方案是每班配备五名医生，其中两名医生专门负责绿色患者。
通过平衡医生对绿色和黄色患者的分配，并使医生的可利用性与预测的需求模式相匹配，可以减少等待时间。急诊科的模拟可用于生成和测试减少过度拥挤的解决方案。
