A system for patient management based discrete-event simulation and hierarchical clustering

Hospital Accident and Emergency (A&E) departments in England have a 4 hour target to treat 98% of patients from arrival to discharge, admission or transfer. Managing resources to meet the target and deliver care across the range of A&E services is a huge challenge for A&E managers. This paper develops an intelligent patient management tool to help managers and clinicians better understand patient length of stay and resources within an A&E area. The developed discrete-event simulation model gives a highlevel representation of ambulance arrivals into A&E. The model facilitates analysis in the following ways: visually interactive software showing patient length of stay in the A&E area; patient activity broken down into sub-groups so that intelligence might be gathered on how sub-groups affect the overall length of stay; understanding the number of patient treatment places and nurse resources required. To support ease of inputs for scenario and sensitivity testing, data is entered into the simulation model (Simul8) via Excel spreadsheets. The model discussed in this paper used patient length of stay grouped by A&E diagnosis codes and was limited to ambulance arrivals. The analysis was derived from A&E attendance in 2004 from an English hospital

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

A healthcare space planning simulation model for Accident and Emergency (A&E)

The National Health Service (NHS) in the United Kingdom provides a range service for its population including primary care and hospital services. The impact of the 2008 economic and financial crises prompted a tightening of public budgets including health. Over the next few years, and most likely beyond, the NHS is planning for unprecedented levels of efficiency saving in the order of £ billions. With little doubt, the NHS will need to review its way of working will need to do more with less. Simulation is an established technique with applications in many industries including healthcare. Potentially, there are huge opportunities for simulation use to make further inroads in the field of healthcare. Despite the potential, arguably, simulation has failed to make a significant impact in health. Some evidence has tended to suggest that within health there has been poor adaption along with poor linkage to real-world problems, as perceived by healthcare stakeholders. The aim of this thesis is to develop a model to help address real-world healthcare issues as recognised by healthcare stakeholders. In doing so, this thesis will focus on a couple of real-world problems, namely: What space is needed to meet service demand, when is it needed and what will it cost? What space do we have, how can it be used to meet service demand and at what cost? The developed simulation space demand model will demonstrate its value modelling dynamic systems over static models. The developed models will also show its value highlighting space demand issues by groups of patients, by time of day. Real, readily available data (arrival and length of stay, by patient group) would drive the model inputs, supporting ease of use and clarity for healthcare stakeholders. The model was modular by design to support rapid reconfiguration. Dynamically modelled space information allows service managers and Healthcare Planners to better manage and organise their space in a flexible way to meet service requirements. This work will also describe how space demand can linked with building notes to determine Schedules of Accommodation which can be used to cost floor space and consequent building or refurbishment costs. Furthermore, this information could be used to drive business plans and to develop operational cost pertaining to the floor area. This body of work debates using function-to-space ratios and attaching facilities management cost. Our findings suggest great variance in function-to-space ratios. Our findings also suggest that moving to median or lower quartile function-to-space ratios could potentially save hospitals £ millions in facilities management costs. This thesis will reflect on the level of modelling taking place in the healthcare industry by non-academic healthcare modellers, sometimes collectively known as Healthcare Planners, the Healthcare Planning role in space planning and their links with healthcare stakeholders. This reflection will also consider whether healthcare stakeholders perceive a great need for academic healthcare modelling, if they believe their modelling needs are met by Healthcare Planners. A central theme of this thesis is that academic modelling and Healthcare Planning have great synergy and that bringing together Healthcare Planners’ industry knowledge and stakeholder relationships with academic know-how, can make a significant contribution to the healthcare simulation modelling arena

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