The DH Accident and Emergency Department model: a national generic model used locally

Abstract

The Department of Health (DH) Accident and Emergency (A&E) simulation model was developed by Operational Research analysts within DH to inform the national policy team of significant barriers to the national target for 98% of all A&E attendances to be completed (discharged, transferred or admitted) within four hours of arrival in England by December 2004. This paper discusses why the model was developed, the structure of the model, and the impact when used to inform national policy development. The model was then used as a consultancy tool to aid struggling hospital trusts to improve their A&E departments. The paper discusses these experiences with particular reference to the challenges of using a 'generic' national model for 'specific' local use.

Maximising Patient Throughput using Discrete-event Simulation

“…Studies include making strategic decisions for various departments (Ballard and Kuhl, 2006;Denton et al, 2006;Vanberkel and Blake, 2007;Leskovar et al, 2011); estimating capacity levels and measuring waiting times (Werker et al, 2009); analysing patient flows (Brenner et al, 2010;Zeng et al, 2012); measuring policy impact (Fletcher et al, 2007); and simulating patient scheduling and utilisations (Harper and Gamlin, 2003;Werker et al, 2009;Lee et al, 2013;Quevedo and Chapilliquén, 2014). It has been argued that the extensive use of process modelling is limited in healthcare compared with other industries (Harper and Pitt, 2004) due to the complexity of the processes and the vast amounts of data required to provide accurate models (Antonacci et al, 2016).…”

“…For example, users can simulate and compare between 24 clinic rooms and 36 clinic rooms with ease. Previous academic discussion has noted that tools for this type of modelling are better understood by users if the inputs have sensible default values from the outset (Fletcher et al, 2007;Gunal and Pidd, 2010). In acknowledgement of this, the tool was developed with default values for each input derived from discussions with the Trust and analysis of the historical data.…”

Modeling and simulating hospital operations in a 3D environment

“…However, there are also dashboards which use simulation technologies to inform, guide and predict how clinical care and facility management may be impacted in a variety of scenarios. These have included studies looking at: improving radiation therapy planning (Werker et al 2009); reducing waiting times and delays (Harper and Gamlin 2003;Vanberkel and Blake 2007;Al-Araidah, Boran, and Wahsheh 2012); modelling outpatient departments (Quevedo and Chapilliquén 2015); modelling emergency departments (Zeng et al 2012;Cabrera et al 2012;Brenner et al 2010); maximizing utilization and capacities (Lee et al 2013;Ballard and Kuhl 2006); and measuring the impact of policy changes (Fletcher et al 2007).…”

“…Previous healthcare related dashboards have been developed to analyze bed occupancies (Daley et al 2013), readmission prevention (Stadler et al 2016) and performance management (Mesabbah and Arisha 2016). Other dashboards have been built as Discrete-Event Simulators (DES) to estimate capacity levels (Werker et al 2009), measure policy impact (Fletcher et al 2007), simulate patient scheduling (Lee et al 2013;Werker et al 2009;Quevedo and Chapilliquén 2015;Harper and Gamlin 2003) and aid strategic decision making (Leskovar et al 2011;Vanberkel and Blake 2007;Ballard and Kuhl 2006).…”

“…Quality dashboards, however, provide productivity and quality indicators to managers in a visual manner, enabling decision makers in healthcare organizations to identify aspects that can be improved for efficiency, patient care or cost (Dowding et al 2015). Examples of quality dashboards include Fletcher et al (2007), Lee et al (2013), andAl-Araidah, Boran, andWahsheh (2012).…”