Using simulation to help hospitals reduce emergency department waiting times: Examples and impact

Abstract

In recent years, all acute hospitals in the UK have experienced unprecedented emergency department waiting times and hospital bed pressures. The consequences are overcrowded emergency departments, ambulance shortages, cancelled elective operations, low staff morale and financial penalties. To deal with the increasing numbers of patient admissions and delayed discharges hospitals must turn now to modelling and simulation to help increase their flexibility and ability to deal with demand variation. Hospitals face several issues that reduce their flexibility including the need for extreme value-for-money and specialization of care. This talk presents three ED case studies undertaken by an analytics team in the UK. The paper considers the impact of the work and challenges arising from their experiences of simulation modelling in acute hospitals. Final thoughts consider the future of ED simulation. INTRODUCTIONEmergency department (ED) overcrowding is common in the United Kingdom's (UK) National Health Service (NHS). In England, performance of EDs are publicly reported using the (infamous) four hour waiting (cycle) time target. The target was originally set to a 100% level of compliance, but was quickly relaxed to 98% in 2004 and then 95% from 2010. The pressure felt in an NHS ED is closely linked to bed pressure within the main hospital (Cooke et al. 2004). Bed pressures and subsequent poor ED performance are due to systemic issues ranging from capacity in community based care, such as social services and nursing homes, lack of investment in public health, and the aging population. While the long term solutions to the systemic problem are sought, hospitals must find ways to increase their short term flexibility to deal with variation in demand and length of patient stay. Many of the flexibility issues experienced by hospitals can be conceptualized as queuing problems. Modelling and Simulation (M&S) therefore has a key role to play in finding solutions that ease the pressure on ED's and hospitals. M&S has been used extensively to study EDs (Gul and Guneri 2015). The majority of studies take a discrete-event simulation approach (DES; e.g. Coats and Michalis 2001;Günal and Pidd 2009;Paul and Lin 2012;Chavis et al. 2016;Oh et al. 2016;Wong et al. 2016;Yang et al. 2016). DES studies have typically focused on the number and scheduling of resources within the ED (Saghafian et al. 2015). Few studies appear to have worked with hospitals to examine the flexibility of the ED and associated processes to deal with variation in demand. With the exception of a few large centers in the UK, hospitals are unlikely to have the finance for substantial increases in their ED staffing. Hence the efficiency and flexibility of processes has become ever more important. This paper first presents some of the challenges UK hospitals face in managing demand and the options they have used in order to have the flexibility to meet waiting time targets in the face of hospital bed pressures. We describe available data sources for M&S and deta...

Simheuristic and learnheuristic algorithms for the temporary-facility location and queuing problem during population treatment or testing events

“…The resulting methodology was able to provide realistic predictions for the system behaviour under different scenarios. Another example of using simulation modeling for reducing waiting times in healthcare was addressed by Monks and Meskarian (2017). The authors concluded that reducing waiting times when emergency departments (EDs) are of low performance is hard to get.…”

Optimizing Sugar Manufacturing: A Hybrid Simulation-Based Approach and MCDM for Efficient Decision Making

“…By employing the simulation model developed by Perez et al [22], clinics have the opportunity to develop protocols that can minimize waiting times for patients by scheduling doctors and medical assistants according to demand fluctuations. To address issues such as overcrowded emergency departments, Monks and Meskarian [23] used simulation modeling because of its flexibility and ability to handle varying demands in a complex system to solve the problem of overcrowded emergency departments, ambulance shortages, canceled elective surgeries, low staff morale, and financial penalties faced by acute hospitals in the UK.…”

Towards generic modelling of hospital wards: Reuse and redevelopment of simple models

“…The educational impact of the model was not an original aim of the study. We, therefore, did not have a substantive educational intervention to hand such as that from SimLean (Robinson, Radnor, Burgess, & Worthington, 2012); however, we did have simple queuing models that could be deployed from working in acute hospitals on similar problems (Monks & Meskarian, 2017). It is interesting to note that the queuing education we provided was to explain the results of the (simple) archetype simulation model.…”

“…Application in multiplesettings can expose the weaknesses in the design of a generic model and the adaptions that are needed (Robinson et al, 2004). The requirement to recode the, possibly complex, generic model reduces the likelihood of opportunistic reuse and testing of a model by new modelling teams substantially (Monks & Meskarian, 2017). Our approach is to make computer implementations of the model findable, accessible and citable.…”