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in the hospital we were able to predict likely occupancy and densities of key spaces throughout the day. By identifying key scenarios, such as peak visitor hours, we could inform and challenge fundamental aspects of the design such as lift provision. The modelling output was interactively

used to assess the performance of the designs as they evolved, optimising the building layout, bed-room locations, corridor and stair widths and lift size/numbers and locations against parameters such as travel distances, waiting times, queues and bed-bed conflicts in corridors. A key outcome of our study resulted in a

major cost-saving through optimising corridor widths. Following HBN guidance, corridors where two beds need to pass regularly should be a minimum of 2,960 mm (Department of Health Estates & Facilities Division, 2007). By modelling the movements and activities during a typical day we determined the likely frequency of beds passing in each corridor of the hospital and therefore which corridors could be optimised due to low activity and which corridors should remain the recommended width due to higher activity levels. This approach can similarly to applied to design proposals for other hospital typologies to optimise designs for user experience and construction and operational costs/efficiency.

Outpatient department – Royal United Hospital, Bath The Royal United Hospital is undergoing major changes to provide a new Therapies and Cancer Unit. Part of the new works will require the re-housing of a number of outpatient departments from buildings, which are being demolished. The Trust is keen to maximise the utilisation of its estate and minimise the need for new build by accommodating these displaced departments within the remainder of the existing hospital. As opposed to a centralised outpatient

department, the hospital operates under a ‘clinical village’ model with co-located inpatient wards and outpatient departments for each speciality distributed across the site. While this configuration provides many benefits, in-particular allowing consultants to

oversee both outpatients and inpatients efficiently, there tends to be large fluctuations in space utilisation throughout the week. Our research team undertook an extensive

space utilisation exercise across the outpatient departments to identify opportunities to use space more effectively, minimising the need for new build. To model the movements and activities within each department we gathered data from a range of sources including interviews with clinical staff to understand patient processes; comprehensive surveys captured arrival profiles, frequency of activities, dwell times and occupancies of waiting areas; and anonymised historical patient data from the hospitals patient logging system covering a period of 12+ months. The results from this study highlighted

opportunities to significantly improve space utilisation within the hospital. For example, through merging departments and optimising clinical timetabling. We are continuing to work with the Trust and architects to inform decisions regarding merging departments through the use of dynamic modelling. This allows us to interrogate potential options in the virtual environment with respect to multiple key performance indicators such as occupancy of waiting room, journey times, space utilisation and waiting times. This approach enables us to assess each option for a range of ‘what-if’ scenarios, including increase in patient demand, alternative processes and resource provisions, to ensure the final solution is resilient to future changes.

Conclusion 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. This approach contributes to improved performance of existing hospitals and optimises designs for new build. In addition, integration with data capturing, analysis and modelling tools enables delivery of ongoing support via a ‘dashboard’ for live performance monitoring

‘The methodology offers an integrated assessment of spaces, people and processes to improve space utilisation, process efficiency and patient experience.’

and bottleneck assessment. These tools and techniques can be employed in a holistic way to bring about real, measurable improvements in the quality of services and at the same time reduce costs in this complex and dynamic environment.

References • Abdur Rais AV. (2010). Operations Research in Healthcare: a survey. International Transactions in Operational Research, pp. 1-31.

• Bacon M. (2012). Occupancy analytics - a new science for energy efficient hospital design. Health and Care Infrastructure and Innovation Centre Conference 2012, (pp. 67-75). Cardiff.

• F Pascale NA. Evaluation, analysis and benchmarking of the design of emergency departments in Italy. Health and Care Infrastructures and Innovation Centre International Conference 2012 (pp. 93-99). Cardiff.

• Pidd MM. Discrete event simulation for performance modelling in health care: a review of the literature. Journal of Simulation 2010.

• Brailsford SC. An analysis of the academic literature on simulation and modelling in health care. Journal of Simulation 2009; 130-140.

• Zhengwei Li, Y. H. (2009). HVAC Design Informed By Organizational Simulation. Building Simulation, (pp. 2198-2203). Glasgow.

• Sharma SB. A static-dynamic network model for crowd flow simulation. In: 6th International Symposium on Space Syntax. 12-15 Jun 2007, Istanbul, Turkey.

• Department of Health Estates & Facilities Division (2007). Health Building Note 00-04 – Circulation and communication spaces (pp. 5).

Providing insights into the vast field of healthcare engineering and facility management


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