FACILITY DESIGN
CCTV footage of Queen’s Hospital A&E main waiting area during a peak period (above); Typical patient process for Triage vs. RAT model (right); Dynamic modelling of patient flows in Queen’s A&E department (bottom right).
despite evidence suggesting that conventional design practices over-estimate building occupancy and thus space is inappropriately designed and allocated (Bacon, 2012; Zhengwei Li, 2009). Spatial analysis, including agent based
modelling methods, should be able to contribute to the improvement of efficiency and effectiveness of hospitals by helping to design spaces that more closely meet their functional requirements with respect to space provision, accessibility, way-finding and safety (F. Pascale, 2012). These analyses may be able to identify opportunities to reduce wasted space, unnecessary movement and improve the conditions people experience while waiting or moving around a hospital. Sharma (2007) demonstrated an implementation of dynamic agent based simulation to evaluate, benchmark and enhance patient, staff and visitor movements within hospitals. A developing agent-based methodology for analysing and informing the design and operation of in-patient and out- patient hospitals (including A&E). Three case studies illustrate how the methodology and software implementations can be applied to improving the efficiency of existing hospital processes/management as well as proposed new hospitals.
Methodology The methodology offers an integrated assessment of spaces, people and processes to improve space utilisation, process efficiency and patient experience. This is achieved by optimising layouts, space use and operational processes. The approach follows the stages outlined in Figure 1 that can be tailored to
Triage process Waiting Register
RAT process Register
Waiting Triage Tests Beds Doctor Tests
Doctor Beds
meet the specific needs of existing and new build hospitals and different typologies. Using data from a variety of sources it is
possible to analyse key performance indicators that are used to assess and optimise the performance of a hospital. For example, walking distances, visibility/line of sight, bottlenecks, queue lengths, waiting room densities, lift usage and flexibility. Predictive modelling is provided by a
modelling technique that includes a holistic dynamic modelling of staff, patient and visitor demands/behaviour, integrated with
‘The case studies presented demonstrate the potential for a dynamic agent-based modelling approach to facilitate design and operational management decisions to improve efficiency and user experience of different types of hospitals.’
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spatial layouts and operational processes in a highly visual and interactive framework. This enables rapid optioneering in a workshop environment and what-if analysis of layouts and processes to provide recommendations for improving performance and to improve the decision making process. The process has to be proved effective at
improving performance of existing hospitals and optimising new build design. The use of data capture, analysis and modelling tools also enables effective delivery of ongoing support via a ‘dashboard’ for live performance monitoring and bottleneck assessment. This can lead to evidence-based decision making in operational management and future designs. The following three case studies illustrate
how this methodology has been applied to help hospital management optimise their space use and improve patient, visitor and staff experience in A&E departments, specialist in-patient hospitals and out-patient departments.
IFHE DIGEST 2015
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