Meeting the four-hour deadline in an A&E department

This is the print version of the Article. The official published version can be obtained from the link below - Copyright @ 2011 EmeraldPurpose – Accident and emergency (A&E) departments experience a secondary peak in patient length of stay (LoS) at around four hours, caused by the coping strategies used to meet the operational standards imposed by government. The aim of this paper is to build a discrete-event simulation model that captures the coping strategies and more accurately reflects the processes that occur within an A&E department. Design/methodology/approach – A discrete-event simulation (DES) model was used to capture the A&E process at a UK hospital and record the LoS for each patient. Input data on 4,150 arrivals over three one-week periods and staffing levels was obtained from hospital records, while output data were compared with the corresponding records. Expert opinion was used to generate the pathways and model the decision-making processes. Findings – The authors were able to replicate accurately the LoS distribution for the hospital. The model was then applied to a second configuration that had been trialled there; again, the results also reflected the experiences of the hospital. Practical implications – This demonstrates that the coping strategies, such as re-prioritising patients based on current length of time in the department, employed in A&E departments have an impact on LoS of patients and therefore need to be considered when building predictive models if confidence in the results is to be justified. Originality/value – As far as the authors are aware this is the first time that these coping strategies have been included within a simulation model, and therefore the first time that the peak around the four hours has been analysed so accurately using a model

Building better healthcare – technologies to facilitate evidence-based design processes

Building better healthcare – technologies to facilitate evidence-based design processe

Success and Failure in the Simulation of an Accident and Emergency Department

Healthcare simulation has the potential to offer many benefits but the implementation is often problematic. This paper describes the development of a simulation of an Accident and Emergency Department in an NHS hospital. The early experience of the client provoked great enthusiasm but ultimately the simulation failed to meet all expectations. The simulation delivered a number of benefits, notably in terms of stimulating constructive debate and helping the stakeholders appreciate the complete Accident and Emergency system. The project produced a technically proficient tool that was delivered too late to have the desired impact. This mixed record of success appears typical of many simulations. Important lessons were learned, both technically and in the management of client expectations, which have contributed to subsequent successful implementation in other departments of the hospital. The experience suggests that both potential clients and analysts need to establish realistic expectations and appreciate the particular challenges of simulation in a healthcare environment

Using evidence-based design to improve pharmacy department efficiency.

Using a case study of a pharmacy department rebuild in the South West of England, this article examines the use of evidence-based design to improve the efficiency and staff well-being with a new design. This article compares three designs, the current design, an anecdotal design, and an evidence-based design, to identify how evidence-based design can improve efficiency and staff well-being by reducing walking time and distance. Data were collected from the existing building and used to measure the efficiency of the department in its current state. These data were then mapped onto an anecdotal design, produced by architects from interviews and workshops with the end users, and an evidence-based design, produced by highlighting functions with high adjacencies. This changed the view on the working processes within the department, shifting away from a focus on the existing robotic dispensing system. Using evidence-based design was found to decrease the walking time and distance for staff by 24%, as opposed to the anecdotal design, which increased these parameters by 9%, and is predicted to save the department 248 min across 2 days in staff time spent walking

Developing a conceptual model for police custody in the UK

The reduction of police officers in UK forces in recent years has reduced the number of staff in police custody, hence it is imperative that staff, and other resources, are utilized appropriately to optimize the custody process. This work aims to develop a simulation model that looks at resource utilisation in police custody. Resource availability can be modelled and tested using discrete event simulation. Developing an accurate conceptual model is a key stage to link the real-world problem and the simulation model. There is minimal literature presenting an accurate police custody conceptual model and hence, that is the focus of this paper. The key sources of information for constructing this model, previous literature, custody record analysis and a custody suite visit are discussed. A final conceptual model is presented with a discussion of how this will be converted into a simulation

IMPLEMENTACIÓN EN SIMUL8 DE UN MODELO SOBRE LOS DEPARTAMENTO DE ACCIDENTES Y EMERGENCIAS

Los departamentos de accidentes y emergencias son de gran interés en el área de la simulación de eventos discretos ya que por la importancia de los servicios que prestan se hace apremiante encontrar alternativas que hagan que su rendimiento en cuanto a tiempos de atención sea lo más óptimo posibles. En este trabajo se implementa en el software Simul8 un modelo genérico desarrollado por Günal y Pidd (2006) sobre estos departamentos con el fin de comprender el funcionamiento de estos sistemas y obtener respuestas de investigación sobre la implementación de modelos genéricos y la pertinencia de la simulación de eventos discretos para este tipo de sistemas.The departments of accidents and emergencies are very interesting in simulation and discrete events areas because the importance of the services that they give became important to find alternatives to make their performance in time attention the most optimal. In this work it’s carried out in the software Simul8 a generic model developed by Günal y Pidd (2006) about these departments in order to understand the performance of these systems and to get research answers about the carrying out of generic models and the relevance of simulation of discrete events for this kind of systems

A decision support system for demand and capacity modelling of an accident and emergency department

© 2019 Operational Research Society.Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.Peer reviewe

Roles of pathway-based models and their contribution to the redesign of health-care systems

Care pathways provide a practical analytical tool that encompasses both organizational efficiency and individual patients'care. In the UK, constructing the care pathway has been a recommended starting point for the re-design of health-caresystems. This paper examines the re-design cycle for health-care systems and looks at the role of pathway-basedmodels in the design and operation phases of the cycle. In addition, the models provide further benefits for communicatingrecommended practice and audit of care and outcomes. The models span the classic care pathway with extensions tosimulation modelling. An example of the use of care pathways in the re-design of an emergency department is used forillustration. This study shows the role of pathway models as: a tool for re-design, a catalyst for enhancing communicationand as a repository for audit information. The final role of a tool for modelling contingencies was not implemented. Fromthe example it can be concluded that sophisticated models can be useful, in some applications; however, the simplerapproaches may often be the best, offering rapid, transparent recommendations based on a multidisciplinary approach

Maximising patient throughput using discrete-event simulation

As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis

Maximising patient throughput using discrete-event simulation

As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their department by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis