Reducing overcrowding in an emergency department: a pilot study

Abstract

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.

Main Features and Control Strategies to Reduce Overcrowding in Emergency Departments: A Systematic Review of the Literature

“… 5 At this stage, work organization is comparable with optimization of staff and resources already present, especially when financial tension and limited resources challenge hospitals to provide timely and effective care. 3–15 Beyond optimizing available resources, the most effective strategies to reduce overcrowding are early and targeted interventions, such as: earlier identification of subtle presentations of life threats, earlier starting of diagnostic testing, earlier administration of important therapies, 12 real-time information on ED overcrowding, 16 creating a tool that predicts waiting times to inform patients, 17 reducing the number of potentially avoidable diagnostic tests and treatments performed, 5 discharging patients before noon, 18 re-evaluating all patients staying in hospital for 14 days or more to facilitate their discharge. 19 The proposed solutions, although effective, are not exempt from evident limitations: arbitrary choice of ED LOS threshold values, 16 changes implemented only in an urban and medium-sized ED, 20 implementation of improvement actions in an ED already functioning, 18 ignoring ED patients’ satisfaction or the impact of physician bias on waiting time.…”

“…23 A difficult and delayed access by patients to diagnostic tests prescribed by their family doctor can lead to improper ED use and its consequent overcrowding. Ansah et al 24 and Ferreira Amorim et al 17 concluded that there is an inappropriate ED use and that an enhanced primary care system is needed to reduce ED crowding. Indeed, Wang et al 6 found that families continue to use ED to obtain care for elderly or disabled patients, whose needs should be met in a long-term care facility.…”

“…Ansah et al 24 employed the dynamic system (SD), creating virtual simulation models in order to develop and test the solution strategies before their implementation. Simul8 17 is a model also capable of generating solutions to help healthcare managers to improve ED overcrowding in different scenarios. This forecasting and simulation model is able to predict the patient’s requests for admission and ED waiting time, to assist in human resources planning, process design, to guide educational strategies for staff and to improve decision making of health care managers.…”

“…This forecasting and simulation model is able to predict the patient’s requests for admission and ED waiting time, to assist in human resources planning, process design, to guide educational strategies for staff and to improve decision making of health care managers. 17 The simulation of discrete events (DES) 26 is another model used to explore the impact of the implementation of different strategies on ED flow patients. Another system that acts primarily on productivity is the Dynamic Grouping and Priority Algorithm (DGP), 28 which identifies the most appropriate patient groups for hospitalization, assigning them a priority level based on patient information and organization of the ED in real time.…”

Untitled

“…By evenly allocating physicians to green and yellow patients and matching their availability to anticipated demand patterns, waiting times can be decreased. Solutions to reduce overcrowding can be developed and tested using simulations of EDs (17).…”